Fuzzy Logic Toolbox User`s Guide

Fuzzy Logic Toolbox
For Use with MATLAB
®
Computation
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User’s Guide
Version 2
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Fuzzy Logic Toolbox User’s Guide
 COPYRIGHT 1995 - 1999 by The MathWorks, Inc.
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Printing History: January 1995
April 1997
January 1998
January 1999
First printing
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Forward
The past few years have witnessed a rapid growth in the number and variety
of applications of fuzzy logic. The applications range from consumer products
such as cameras, camcorders, washing machines, and microwave ovens to
industrial process control, medical instrumentation, decision-support systems,
and portfolio selection.
To understand the reasons for the growing use of fuzzy logic it is necessary,
first, to clarify what is meant by fuzzy logic.
Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a
logical system, which is an extension of multivalued logic. But in a wider
sense—which is in predominant use today—fuzzy logic (FL) is almost
synonymous with the theory of fuzzy sets, a theory which relates to classes of
objects with unsharp boundaries in which membership is a matter of degree.
In this perspective, fuzzy logic in its narrow sense is a branch of FL. What is
important to recognize is that, even in its narrow sense, the agenda of fuzzy
logic is very different both in spirit and substance from the agendas of
traditional multivalued logical systems.
In the Fuzzy Logic Toolbox, fuzzy logic should be interpreted as FL, that is,
fuzzy logic in its wide sense. The basic ideas underlying FL are explained very
clearly and insightfully in the Introduction. What might be added is that the
basic concept underlying FL is that of a linguistic variable, that is, a variable
whose values are words rather than numbers. In effect, much of FL may be
viewed as a methodology for computing with words rather than numbers.
Although words are inherently less precise than numbers, their use is closer to
human intuition. Furthermore, computing with words exploits the tolerance
for imprecision and thereby lowers the cost of solution.
Another basic concept in FL, which plays a central role in most of its
applications, is that of a fuzzy if-then rule or, simply, fuzzy rule. Although
rule-based systems have a long history of use in AI, what is missing in such
systems is a machinery for dealing with fuzzy consequents and/or fuzzy
antecedents. In fuzzy logic, this machinery is provided by what is called the
calculus of fuzzy rules. The calculus of fuzzy rules serves as a basis for what
might be called the Fuzzy Dependency and Command Language (FDCL).
Although FDCL is not used explicitly in Fuzzy Logic Toolbox, it is effectively
one of its principal constituents. In this connection, what is important to
Forward
recognize is that in most of the applications of fuzzy logic, a fuzzy logic solution
is in reality a translation of a human solution into FDCL.
What makes the Fuzzy Logic Toolbox so powerful is the fact that most of
human reasoning and concept formation is linked to the use of fuzzy rules. By
providing a systematic framework for computing with fuzzy rules, the Fuzzy
Logic Toolbox greatly amplifies the power of human reasoning. Further
amplification results from the use of MATLAB and graphical user interfaces –
areas in which The MathWorks has unparalleled expertise.
A trend which is growing in visibility relates to the use of fuzzy logic in
combination with neurocomputing and genetic algorithms. More generally,
fuzzy logic, neurocomputing, and genetic algorithms may be viewed as the
principal constituents of what might be called soft computing. Unlike the
traditional, hard computing, soft computing is aimed at an accommodation
with the pervasive imprecision of the real world. The guiding principle of soft
computing is: Exploit the tolerance for imprecision, uncertainty, and partial
truth to achieve tractability, robustness, and low solution cost. In coming
years, soft computing is likely to play an increasingly important role in the
conception and design of systems whose MIQ (Machine IQ) is much higher than
that of systems designed by conventional methods.
Among various combinations of methodologies in soft computing, the one which
has highest visibility at this juncture is that of fuzzy logic and neurocomputing,
leading to so-called neuro-fuzzy systems. Within fuzzy logic, such systems play
a particularly important role in the induction of rules from observations. An
effective method developed by Dr. Roger Jang for this purpose is called ANFIS
(Adaptive Neuro-Fuzzy Inference System). This method is an important
component of the Fuzzy Logic Toolbox.
The Fuzzy Logic Toolbox is highly impressive in all respects. It makes fuzzy
logic an effective tool for the conception and design of intelligent systems. The
Fuzzy Logic Toolbox is easy to master and convenient to use. And last, but not
least important, it provides a reader-friendly and up-to-date introduction to the
methodology of fuzzy logic and its wide-ranging applications.
Lotfi A. Zadeh
Berkeley, CA
January 10, 1995
Contents
Before You Begin
What Is the Fuzzy Logic Toolbox? . . . . . . . . . . . . . . . . . . . . . . . . . 6
How to Use This Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Typographical Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Introduction
1
What Is Fuzzy Logic? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Why Use Fuzzy Logic? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
When Not to Use Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . .
What Can the Fuzzy Logic Toolbox Do? . . . . . . . . . . . . . . . . . . .
1-2
1-5
1-6
1-6
An Introductory Example: Fuzzy vs. Non-Fuzzy . . . . . . . . . 1-8
The Non-Fuzzy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9
The Fuzzy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-13
Some Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-14
Tutorial
2
The Big Picture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Foundations of Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Membership Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Logical Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20
20
24
28
i
If-Then Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Fuzzy Inference Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dinner for Two, Reprise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Fuzzy Inference Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Customization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
37
42
43
Building Systems with the Fuzzy Logic Toolbox . . . . . . . . . .
Dinner for Two, from the Top . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The FIS Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Membership Function Editor . . . . . . . . . . . . . . . . . . . . . . . . .
The Rule Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Rule Viewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Surface Viewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Importing and Exporting from the GUI Tools . . . . . . . . . . . . . . .
Customizing Your Fuzzy System . . . . . . . . . . . . . . . . . . . . . . . . .
45
45
48
49
52
56
59
61
62
63
Working from the Command Line . . . . . . . . . . . . . . . . . . . . . . .
System Display Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Building a System from Scratch . . . . . . . . . . . . . . . . . . . . . . . . . .
FIS Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The FIS Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
67
70
73
73
Working with Simulink . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
An Example: Water Level Control . . . . . . . . . . . . . . . . . . . . . . . . 78
Building Your Own Fuzzy Simulink Models . . . . . . . . . . . . . . . . 83
Sugeno-Type Fuzzy Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
An Example: Two Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
anfis and the ANFIS Editor GUI . . . . . . . . . . . . . . . . . . . . . . . . .
A Modeling Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Model Learning and Inference Through ANFIS . . . . . . . . . . . . .
Familiarity Breeds Validation: Know Your Data . . . . . . . . . . . . .
Some Constraints of anfis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The ANFIS Editor GUI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ANFIS Editor GUI Example 1:
ii
Contents
92
92
93
94
95
95
Checking Data Helps Model Validation . . . . . . . . . . . . . . . . . . . 98
ANFIS Editor GUI Example 2:
Checking Data Doesn’t Validate Model . . . . . . . . . . . . . . . . . . 106
anfis from the Command Line . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
More on anfis and the ANFIS Editor GUI . . . . . . . . . . . . . . . . . 114
Fuzzy Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Fuzzy C-Means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Subtractive Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
Stand-Alone C-Code Fuzzy Inference Engine . . . . . . . . . . . . 130
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
Reference
3
GUI Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Membership Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FIS Data Structure Management . . . . . . . . . . . . . . . . . . . . . . . .
Advanced Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Simulink Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Demos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3-2
3-2
3-3
3-4
3-4
3-5
iii
iv
Contents
Before You Begin
What Is the Fuzzy Logic Toolbox?
How to Use This Guide . . . .
Installation . . . . . . . . .
Typographical Conventions . .
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2
3
3
4
Before You Begin
This section describes how to use the Fuzzy Logic Toolbox. It explains how to
use this guide and points you to additional books for toolbox installation
information.
What Is the Fuzzy Logic Toolbox?
The Fuzzy Logic Toolbox is a collection of functions built on the MATLAB ®
numeric computing environment. It provides tools for you to create and edit
fuzzy inference systems within the framework of MATLAB, or if you prefer you
can integrate your fuzzy systems into simulations with Simulink®, or you can
even build stand-alone C programs that call on fuzzy systems you build with
MATLAB. This toolbox relies heavily on graphical user interface (GUI) tools to
help you accomplish your work, although you can work entirely from the
command line if you prefer.
The toolbox provides three categories of tools:
• Command line functions
• Graphical, interactive tools
• Simulink blocks and examples
The first category of tools is made up of functions that you can call from the
command line or from your own applications. Many of these functions are
MATLAB M-files, series of MATLAB statements that implement specialized
fuzzy logic algorithms. You can view the MATLAB code for these functions
using the statement
type function_name
You can change the way any toolbox function works by copying and renaming
the M-file, then modifying your copy. You can also extend the toolbox by adding
your own M-files.
Secondly, the toolbox provides a number of interactive tools that let you access
many of the functions through a GUI. Together, the GUI- based tools provide
an environment for fuzzy inference system design, analysis, and
implementation.
The third category of tools is a set of blocks for use with the Simulink
simulation software. These are specifically designed for high speed fuzzy logic
inference in the Simulink environment.
6
How to Use This Guide
If you are new to fuzzy logic, begin with Chapter 1, “Introduction.” This
chapter introduces the motivation behind fuzzy logic and leads you smoothly
into the tutorial.
If you are an experienced fuzzy logic user, you may want to start at the
beginning of Chapter 2, “Tutorial,” to make sure you are comfortable with the
fuzzy logic terminology in the Fuzzy Logic Toolbox. If you just want an
overview of each graphical tool and examples of specific fuzzy system tasks,
turn directly to the section in Chapter 2 entitled, “Building Systems with the
Fuzzy Logic Toolbox.” This section does not include information on the
adaptive data modeling application covered by the toolbox function anfis. The
basic functionality of this tool can be found in the section in Chapter 2 entitled,
“anfis and the ANFIS Editor GUI.”
If you just want to start as soon as possible and experiment, you can open an
example system right away by typing
fuzzy tipper
This brings up the Fuzzy Inference System (FIS) editor for an example decision
making problem that has to do with how to tip in a restaurant.
All toolbox users should use Chapter 3, “Reference,” for information on specific
tools or functions. Reference descriptions include a synopsis of the function’s
syntax, as well as a complete explanation of options and operation. Many
reference descriptions also include helpful examples, a description of the
function’s algorithm, and references to additional reading material. For
GUI-based tools, the descriptions include options for invoking the tool.
Installation
To install this toolbox on a workstation or a large machine, see the Installation
Guide for UNIX. To install the toolbox on a PC or Macintosh, see the
Installation Guide for PC and Macintosh.
To determine if the Fuzzy Logic Toolbox is already installed on your system,
check for a subdirectory named fuzzy within the main toolbox directory or
folder.
7
Before You Begin
Typographical Conventions
To Indicate
This Guide Uses
Example
Example code
Monospace type
(Use Code tag.)
To assign the value 5 to A,
enter
A = 5
Function
names
Monospace type
(Use Code tag.)
The cos function finds the
cosine of each array
element.
Function
syntax
Monospace type for text
that must appear as
shown. (Use Code tag.)
The magic function uses
the syntax
M = magic(n)
Monospace italics for
components you can
replace with any variable.
(Use Code-ital tag.)
Keys
Boldface with an initial
capital letter
(Use Menu-Bodytext tag.)
Press the Return key.
Mathematical
expressions
Variables in italics.
This vector represents the
polynomial
MATLAB
output
Functions, operators, and
constants in standard
type. (Use
EquationVariables tag.)
Monospace type
(Use Code tag.)
p = x2 + 2x + 3
MATLAB responds with
A=
5
8
To Indicate
This Guide Uses
Example
Menu names,
menu items,
and controls
Boldface with an initial
capital letter
(Use Menu-Bodytext tag.)
Choose the File menu.
New terms
NCS italics
(Use Body text-ital tag.)
An array is an ordered
collection of information.
9
Before You Begin
10
1
Introduction
What Is Fuzzy Logic? . . . . . . .
Why Use Fuzzy Logic? . . . . . . .
When Not to Use Fuzzy Logic . . . .
What Can the Fuzzy Logic Toolbox Do?
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1-2
1-5
1-6
1-6
An Introductory Example: Fuzzy vs. Non-Fuzzy
The Non-Fuzzy Approach . . . . . . . . . . .
The Fuzzy Approach . . . . . . . . . . . . .
Some Observations . . . . . . . . . . . . . .
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1-8
1-9
1-13
1-14
1
Introduction
What Is Fuzzy Logic?
Fuzzy logic is all about the relative importance of precision: How important is
it to be exactly right when a rough answer will do? All books on fuzzy logic
begin with a few good quotes on this very topic, and this is no exception. Here
is what some clever people have said in the past:
Precision is not truth.
—Henri Matisse
Sometimes the more measurable drives out the most important.
—René Dubos
Vagueness is no more to be done away with in the world of logic than friction in
mechanics.
—Charles Sanders Peirce
I believe that nothing is unconditionally true, and hence I am opposed to every
statement of positive truth and every man who makes it.
—H. L. Mencken
So far as the laws of mathematics refer to reality, they are not certain. And so
far as they are certain, they do not refer to reality.
—Albert Einstein
As complexity rises, precise statements lose meaning and meaningful statements
lose precision.
—Lotfi Zadeh
Some pearls of folk wisdom also echo these thoughts:
Don’t lose sight of the forest for the trees.
Don’t be penny wise and pound foolish.
The Fuzzy Logic Toolbox for use with MATLAB is a tool for solving problems
with fuzzy logic. Fuzzy logic is a fascinating area of research because it does a
good job of trading off between significance and precision—something that
humans have been managing for a very long time.
Fuzzy logic sometimes appears exotic or intimidating to those unfamiliar with
it, but once you become acquainted with it, it seems almost surprising that no
one attempted it sooner. In this sense fuzzy logic is both old and new because,
1-2
What Is Fuzzy Logic?
although the modern and methodical science of fuzzy logic is still young, the
concepts of fuzzy logic reach right down to our bones.
Precision and Significance in the Real World
A 1500 kg mass
is approaching
your head at
45.3 m/sec.
Precision
LOOK
OUT!!
Significance
Fuzzy logic is a convenient way to map an input space to an output space. This
is the starting point for everything else, and the great emphasis here is on the
word “convenient.”
What do I mean by mapping input space to output space? Here are a few
examples: You tell me how good your service was at a restaurant, and I’ll tell
you what the tip should be. You tell me how hot you want the water, and I’ll
adjust the faucet valve to the right setting. You tell me how far away the
subject of your photograph is, and I’ll focus the lens for you. You tell me how
fast the car is going and how hard the motor is working, and I’ll shift the gears
for you.
1-3
1
Introduction
A graphical example of an input-output map is shown below.
Input Space
Output Space
(all possible service
quality ratings)
tonight's service
quality
(all possible tips)
Black
Box
the “right” tip
for tonight
An input-output map for the tipping problem:
“Given the quality of service, how much should I tip?”
It’s all just a matter of mapping inputs to the appropriate outputs. Between the
input and the output we’ll put a black box that does the work. What could go in
the black box? Any number of things: fuzzy systems, linear systems, expert
systems, neural networks, differential equations, interpolated
multi-dimensional lookup tables, or even a spiritual advisor, just to name a few
of the possible options. Clearly the list could go on and on.
Of the dozens of ways to make the black box work, it turns out that fuzzy is
often the very best way. Why should that be? As Lotfi Zadeh, who is considered
to be the father of fuzzy logic, once remarked: “In almost every case you can
build the same product without fuzzy logic, but fuzzy is faster and cheaper.”
1-4
What Is Fuzzy Logic?
Why Use Fuzzy Logic?
Here is a list of general observations about fuzzy logic.
• Fuzzy logic is conceptually easy to understand.
The mathematical concepts behind fuzzy reasoning are very simple. What
makes fuzzy nice is the “naturalness” of its approach and not its far-reaching
complexity.
• Fuzzy logic is flexible.
With any given system, it’s easy to massage it or layer more functionality on
top of it without starting again from scratch.
• Fuzzy logic is tolerant of imprecise data.
Everything is imprecise if you look closely enough, but more than that, most
things are imprecise even on careful inspection. Fuzzy reasoning builds this
understanding into the process rather than tacking it onto the end.
• Fuzzy logic can model nonlinear functions of arbitrary complexity.
You can create a fuzzy system to match any set of input-output data. This
process is made particularly easy by adaptive techniques like ANFIS
(Adaptive Neuro-Fuzzy Inference Systems), which are available in the Fuzzy
Logic Toolbox.
• Fuzzy logic can be built on top of the experience of experts.
In direct contrast to neural networks, which take training data and generate
opaque, impenetrable models, fuzzy logic lets you rely on the experience of
people who already understand your system.
• Fuzzy logic can be blended with conventional control techniques.
Fuzzy systems don’t necessarily replace conventional control methods. In
many cases fuzzy systems augment them and simplify their implementation.
• Fuzzy logic is based on natural language.
The basis for fuzzy logic is the basis for human communication. This
observation underpins many of the other statements about fuzzy logic.
The last statement is perhaps the most important one and deserves more
discussion. Natural language, that which is used by ordinary people on a daily
basis, has been shaped by thousands of years of human history to be convenient
and efficient. Sentences written in ordinary language represent a triumph of
efficient communication. We are generally unaware of this because ordinary
language is, of course, something we use every day. Since fuzzy logic is built
1-5
1
Introduction
atop the structures of qualitative description used in everyday language, fuzzy
logic is easy to use.
When Not to Use Fuzzy Logic
Fuzzy logic is not a cure-all. When should you not use fuzzy logic? The safest
statement is the first one made in this introduction: fuzzy logic is a convenient
way to map an input space to an output space. If you find it’s not convenient,
try something else. If a simpler solution already exists, use it. Fuzzy logic is the
codification of common sense—use common sense when you implement it and
you will probably make the right decision. Many controllers, for example, do a
fine job without using fuzzy logic. However, if you take the time to become
familiar with fuzzy logic, you’ll see it can be a very powerful tool for dealing
quickly and efficiently with imprecision and nonlinearity.
What Can the Fuzzy Logic Toolbox Do?
The Fuzzy Logic Toolbox allows you to do several things, but the most
important thing it lets you do is create and edit fuzzy inference systems. You
can create these systems using graphical tools or command-line functions, or
you can generate them automatically using either clustering or adaptive
neuro-fuzzy techniques.
If you have access to Simulink, you can easily test your fuzzy system in a block
diagram simulation environment.
The toolbox also lets you run your own stand-alone C programs directly,
without the need for Simulink. This is made possible by a stand-alone Fuzzy
Inference Engine that reads the fuzzy systems saved from a MATLAB session.
1-6
What Is Fuzzy Logic?
You can customize the stand-alone engine to build fuzzy inference into your
own code. All provided code is ANSI compliant.
Fuzzy
Inference
System
Fuzzy
Logic
Toolbox
Simulink
Stand-alone
Fuzzy Engine
User-written
M-files
Other toolboxes
MATLAB
Because of the integrated nature of MATLAB’s environment, you can create
your own tools to customize the Fuzzy Logic Toolbox or harness it with another
toolbox, such as the Control System, Neural Network, or Optimization Toolbox,
to mention only a few of the possibilities.
1-7
1
Introduction
An Introductory Example: Fuzzy vs. Non-Fuzzy
A specific example would be helpful at this point. To illustrate the value of
fuzzy logic, we’ll show two different approaches to the same problem: linear
and fuzzy. First we will work through this problem the conventional
(non-fuzzy) way, writing MATLAB commands that spell out linear and
piecewise-linear relations. Then we’ll take a quick look at the same system
using fuzzy logic.
Consider the tipping problem: what is the “right” amount to tip your
waitperson? Here is a clear statement of the problem.
The Basic Tipping Problem. Given a number between 0 and 10 that
represents the quality of service at a restaurant (where 10 is excellent), what
should the tip be?
Cultural footnote: This problem is based on tipping as it is typically practiced
in the United States. An average tip for a meal in the U.S. is 15%, though the
actual amount may vary depending on the quality of the service provided.
1-8
An Introductory Example: Fuzzy vs. Non-Fuzzy
The Non-Fuzzy Approach
Let’s start with the simplest possible relationship. Suppose that the tip always
equals 15% of the total bill.
tip = 0.15
0.25
0.2
tip
0.15
0.1
0.05
0
0
2
4
6
8
10
service
This doesn’t really take into account the quality of the service, so we need to
add a new term to the equation. Since service is rated on a scale of 0 to 10, we
might have the tip go linearly from 5% if the service is bad to 25% if the service
is excellent. Now our relation looks like this:
tip=0.20/10*service+0.05
0.25
tip
0.2
0.15
0.1
0.05
0
2
4
6
8
10
service
1-9
1
Introduction
So far so good. The formula does what we want it to do, and it’s pretty
straightforward. However, we may want the tip to reflect the quality of the food
as well. This extension of the problem is defined as follows:
The Extended Tipping Problem. Given two sets of numbers between 0 and 10
(where 10 is excellent) that respectively represent the quality of the service and
the quality of the food at a restaurant, what should the tip be?
Let’s see how the formula will be affected now that we’ve added another
variable. Suppose we try:
tip = 0.20/20*(service+food)+0.05;
0.25
tip
0.2
0.15
0.1
0.05
10
10
5
food
5
0
0
service
In this case, the results look pretty, but when you look at them closely, they
don’t seem quite right. Suppose you want the service to be a more important
1-10
An Introductory Example: Fuzzy vs. Non-Fuzzy
factor than the food quality. Let’s say that the service will account for 80% of
the overall tipping “grade” and the food will make up the other 20%. Try:
servRatio=0.8;
tip=servRatio*(0.20/10*service+0.05) + ...
(1–servRatio)*(0.20/10*food+0.05);
0.25
tip
0.2
0.15
0.1
0.05
10
10
5
food
5
0
0
service
The response is still somehow too uniformly linear. Suppose you want more of
a flat response in the middle, i.e., you want to give a 15% tip in general, and
will depart from this plateau only if the service is exceptionally good or bad.
This, in turn, means that those nice linear mappings no longer apply. We can
still salvage things by using a piecewise linear construction. Let’s return to the
one-dimensional problem of just considering the service. You can string
together a simple conditional statement using breakpoints like this:
if service<3,
tip=(0.10/3)*service+0.05;
elseif service<7,
tip=0.15;
elseif service<=10,
tip=(0.10/3)*(service–7)+0.15;
end
1-11
1
Introduction
The plot looks like this.
0.25
tip
0.2
0.15
0.1
0.05
0
2
4
6
8
10
service
If we extend this to two dimensions, where we take food into account again,
something like this results:
servRatio=0.8;
if service<3,
tip=((0.10/3)*service+0.05)*servRatio + ...
(1–servRatio)*(0.20/10*food+0.05);
elseif service<7,
tip=(0.15)*servRatio + ...
(1–servRatio)*(0.20/10*food+0.05);
else,
tip=((0.10/3)*(service–7)+0.15)*servRatio + ...
(1–servRatio)*(0.20/10*food+0.05);
end
0.25
tip
0.2
0.15
0.1
0.05
10
10
5
food
1-12
5
0
0
service
An Introductory Example: Fuzzy vs. Non-Fuzzy
Wow! The plot looks good, but the function is surprisingly complicated. It was
a little tricky to code this correctly, and it’s definitely not easy to modify this
code in the future. Moreover, it’s even less apparent how the algorithm works
to someone who didn’t witness the original design process.
The Fuzzy Approach
It would be nice if we could just capture the essentials of this problem, leaving
aside all the factors that could be arbitrary. If we make a list of what really
matters in this problem, we might end up with the following rule descriptions:
1. If service is poor, then tip is cheap
2. If service is good, then tip is average
3. If service is excellent, then tip is generous
The order in which the rules are presented here is arbitrary. It doesn’t matter
which rules come first. If we wanted to include the food’s effect on the tip, we
might add the following two rules:
4. If food is rancid, then tip is cheap
5. If food is delicious, then tip is generous
In fact, we can combine the two different lists of rules into one tight list of three
rules like so:
1. If service is poor or the food is rancid, then tip is cheap
2. If service is good, then tip is average
3. If service is excellent or food is delicious, then tip is generous
These three rules are the core of our solution. And coincidentally, we’ve just
defined the rules for a fuzzy logic system. Now if we give mathematical
meaning to the linguistic variables (what is an “average” tip, for example?) we
would have a complete fuzzy inference system. Of course, there’s a lot left to the
methodology of fuzzy logic that we’re not mentioning right now, things like:
• How are the rules all combined?
• How do I define mathematically what an “average” tip is?
These are questions we provide detailed answers to in the next few chapters.
The details of the method don’t really change much from problem to problem
— the mechanics of fuzzy logic aren’t terribly complex. What matters is what
1-13
1
Introduction
we’ve shown in this preliminary exposition: fuzzy is adaptable, simple, and
easily applied.
0.25
tip
0.2
0.15
0.1
0.05
10
10
5
food
5
0
0
service
Here is the picture associated with the fuzzy system that solves this problem.
The picture above was generated by the three rules above. The mechanics of
how fuzzy inference works is explained “The Big Picture” on page 2-18,
“Foundations of Fuzzy Logic” on page 2-20, and in “Fuzzy Inference Systems”
on page 2-36. In the “Building Systems with the Fuzzy Logic Toolbox” on page
2-45, the entire tipping problem is worked through using the graphical tools in
the Fuzzy Logic Toolbox.
Some Observations
Here are some observations about the example so far. We found a piecewise
linear relation that solved the problem. It worked, but it was something of a
nuisance to derive, and once we wrote it down as code, it wasn’t very easy to
interpret. On the other hand, the fuzzy system is based on some “common
sense” statements. Also, we were able to add two more rules to the bottom of
the list that influenced the shape of the overall output without needing to undo
what had already been done. In other words, the subsequent modification was
pretty easy.
Moreover, by using fuzzy logic rules, the maintenance of the structure of the
algorithm decouples along fairly clean lines. The notion of an average tip might
change from day to day, city to city, country to country, but the underlying logic
is the same: if the service is good, the tip should be average. You can recalibrate
the method quickly by simply shifting the fuzzy set that defines average
without rewriting the fuzzy rules.
1-14
An Introductory Example: Fuzzy vs. Non-Fuzzy
You can do this sort of thing with lists of piecewise linear functions, but there
is a greater likelihood that recalibration will not be so quick and simple.
For example, here is the piecewise linear tipping problem slightly rewritten to
make it more generic. It performs the same function as before, only now the
constants can be easily changed.
% Establish constants
lowTip=0.05; averTip=0.15; highTip=0.25;
tipRange=highTip–lowTip;
badService=0; okayService=3;
goodService=7; greatService=10;
serviceRange=greatService–badService;
badFood=0; greatFood=10;
foodRange=greatFood–badFood;
% If service is poor or food is rancid, tip is cheap
if service<okayService,
tip=(((averTip–lowTip)/(okayService–badService)) ...
*service+lowTip)*servRatio + ...
(1–servRatio)*(tipRange/foodRange*food+lowTip);
% If service is good, tip is average
elseif service<goodService,
tip=averTip*servRatio + (1–servRatio)* ...
(tipRange/foodRange*food+lowTip);
% If service is excellent or food is delicious, tip is generous
else,
tip=(((highTip–averTip)/ ...
(greatService–goodService))* ...
(service–goodService)+averTip)*servRatio + ...
(1–servRatio)*(tipRange/foodRange*food+lowTip);
end
Notice the tendency here, as with all code, for creeping generality to render the
algorithm more and more opaque, threatening eventually to obscure it
completely. What we’re doing here isn’t (shouldn’t be!) that complicated. True,
we can fight this tendency to be obscure by adding still more comments, or
perhaps by trying to rewrite it in slightly more self-evident ways, but the
medium is not on our side.
1-15
1
Introduction
The truly fascinating thing to notice is that if we remove everything except for
three comments, what remain are exactly the fuzzy rules we wrote down
before:
% If service is poor or food is rancid, tip is cheap
% If service is good, tip is average
% If service is excellent or food is delicious, tip is generous
If, as with a fuzzy system, the comment is identical with the code, think how
much more likely your code is to have comments! Fuzzy logic lets the language
that’s clearest to you, high level comments, also have meaning to the machine,
which is why it’s a very successful technique for bridging the gap between
people and machines.
Or think of it this way: by making the equations as simple as possible (linear)
we make things simpler for the machine but more complicated for us. But
really the limitation is no longer the computer—it’s our mental model of what
the computer is doing. We all know that computers have the ability to make
things hopelessly complex; fuzzy logic is really about reclaiming the middle
ground and letting the machine work with our preferences rather than the
other way around. It’s about time.
1-16
2
Tutorial
The Big Picture
. . . . . . . . . . . . . . . . . . 2-2
Foundations of Fuzzy Logic . . . . . . . . . . . . . 2-4
Fuzzy Inference Systems
. . . . . . . . . . . . . . 2-20
Building Systems with the Fuzzy Logic Toolbox . . . . 2-29
Working from the Command Line . . . . . . . . . . 2-49
Working with Simulink . . . . . . . . . . . . . . . 2-62
Sugeno-Type Fuzzy Inference . . . . . . . . . . . . 2-70
anfis and the ANFIS Editor GUI . . . . . . . . . . . 2-76
Fuzzy Clustering . . . . . . . . . . . . . . . . . 2-104
Stand-Alone C-Code Fuzzy Inference Engine . . . . 2-114
Glossary . . . . . . . . . . . . . . . . . . . . . 2-116
References . . . . . . . . . . . . . . . . . . . . 2-118
2
Tutorial
The Big Picture
We’ll start with a little motivation for where we are headed in this chapter. The
point of fuzzy logic is to map an input space to an output space, and the primary
mechanism for doing this is a list of if-then statements called rules. All rules
are evaluated in parallel, and the order of the rules is unimportant. The rules
themselves are useful because they refer to variables and the adjectives that
describe those variables. Before we can build a system that interprets rules, we
have to define all the terms we plan on using and the adjectives that describe
them. If we want to talk about how hot the water is, we need to define the range
that the water’s temperature can be expected to vary over as well as what we
mean by the word hot. These are all things we’ll be discussing in the next
several sections of the manual. The diagram below is something like a roadmap
for the fuzzy inference process. It shows the general description of a fuzzy
system on the left and a specific fuzzy system (the tipping example from the
Introduction) on the right.
The General Case...
A Specific Example...
Input
service
Output
tip
if service is poor then tip is cheap
if service is good then tip is average
if service is excellent then tip is generous
Rules
Input
terms
Output
terms
(interpret)
(assign)
service
is interpreted as
{poor,
good,
excellent}
tip
is assigned to be
{cheap,
average,
generous}
To summarize the concept of fuzzy inference depicted in this figure, fuzzy
inference is a method that interprets the values in the input vector and, based
on some set of rules, assigns values to the output vector.
This chapter is designed to guide you through the fuzzy logic process step by
step by providing an introduction to the theory and practice of fuzzy logic. The
first three sections of this chapter are the most important—they move from
2-18
The Big Picture
general to specific, first introducing underlying ideas and then discussing
implementation details specific to the toolbox. These three areas are
• Foundations of fuzzy logic, which is an introduction to the general
concepts. If you’re already familiar with fuzzy logic, you may want to skip
this section.
• Fuzzy inference systems, which explains the specific methods of fuzzy
inference used in the Fuzzy Logic Toolbox. Since the field of fuzzy logic uses
many terms that do not yet have standard interpretations, you should
consider reading this section just to become familiar with the fuzzy inference
process as it is employed here.
• Building systems with the Fuzzy Logic Toolbox, which goes into detail
about how you build and edit a fuzzy system using this toolbox. This
introduces the graphical user interface tools available in the Fuzzy Logic
Toolbox and guides you through the construction of a complete fuzzy
inference system from start to finish. If you just want to get up to speed as
quickly as possible, start here.
After this there are sections that touch on a variety of topics, such as Simulink
use, automatic rule generation, and demonstrations. But from the point of view
of getting to know the toolbox, these first three sections are the most crucial.
2-19
2
Tutorial
Foundations of Fuzzy Logic
Everything is vague to a degree you do not realize till you have tried to make it
precise. —Bertrand Russell
Fuzzy Sets
Fuzzy logic starts with the concept of a fuzzy set. A fuzzy set is a set without a
crisp, clearly defined boundary. It can contain elements with only a partial
degree of membership.
To understand what a fuzzy set is, first consider what is meant by what we
might call a classical set. A classical set is a container that wholly includes or
wholly excludes any given element. For example, the set of days of the week
unquestionably includes Monday, Thursday, and Saturday. It just as
unquestionably excludes butter, liberty, and dorsal fins, and so on.
Shoe
Polish
Monday
Liberty
Thursday
Butter
Saturday
Dorsal
Fins
Days of the week
We call this set a classical set because it’s been around for such a long time. It
was Aristotle who first formulated the Law of the Excluded Middle, which says
X must either be in set A or in set not-A. Another version runs like this:
Of any subject, one thing must be either asserted or denied.
Here is a restatement of the law with annotations: “Of any subject (say
Monday), one thing (being a day of the week) must be either asserted or denied
(I assert that Monday is a day of the week).” This law demands that opposites,
the two categories A and not-A, should between them contain the entire
universe. Everything falls into either one group or the other. There is no thing
that is both a day of the week and not a day of the week.
2-20
Foundations of Fuzzy Logic
Now consider the set of days comprising a weekend. The diagram below is one
attempt at classifying the weekend days.
Shoe
Polish
Monday
Liberty
Saturday
Sunday
Butter
Friday
Thursday
Dorsal
Fins
Days of the weekend
Most would agree that Saturday and Sunday belong, but what about Friday?
It “feels” like a part of the weekend, but somehow it seems like it should be
technically excluded. So in the diagram above Friday tries its best to sit on the
fence. Classical or “normal” sets wouldn’t tolerate this kind of thing. Either
you’re in or you’re out. Human experience suggests something different,
though: fence sitting is a part of life.
Of course we’re on tricky ground here, because we’re starting to take individual
perceptions and cultural background into account when we define what
constitutes the weekend. But this is exactly the point. Even the dictionary is
imprecise, defining the weekend as “the period from Friday night or Saturday
to Monday morning.” We’re entering the realm where sharp edged yes-no logic
stops being helpful. Fuzzy reasoning becomes valuable exactly when we’re
talking about how people really perceive the concept “weekend” as opposed to
a simple-minded classification useful for accounting purposes only. More than
anything else, the following statement lays the foundations for fuzzy logic:
In fuzzy logic, the truth of any statement becomes a matter of degree.
Any statement can be fuzzy. The tool that fuzzy reasoning gives is the ability
to reply to a yes-no question with a not-quite-yes-or-no answer. This is the kind
of thing that humans do all the time (think how rarely you get a straight
answer to a seemingly simple question) but it’s a rather new trick for
computers.
How does it work? Reasoning in fuzzy logic is just a matter of generalizing the
familiar yes-no (Boolean) logic. If we give “true” the numerical value of 1 and
2-21
2
Tutorial
“false” the numerical value of 0, we’re saying that fuzzy logic also permits
in-between values like 0.2 and 0.7453. For instance:
Q: Is Saturday a weekend day?
A: 1 (yes, or true)
Q: Is Tuesday a weekend day?
A: 0 (no, or false)
Q: Is Friday a weekend day?
A: 0.8 (for the most part yes, but not completely)
Q: Is Sunday a weekend day?
A: 0.95 (yes, but not quite as much as Saturday).
Below on the left is a plot that shows the truth values for “weekend-ness” if we
are forced to respond with an absolute yes or no response. On the right is a plot
that shows the truth value for weekend-ness if we are allowed to respond with
fuzzy in-between values.
1.0
weekend-ness
weekend-ness
1.0
0.0
0.0
Thursday
Friday
Saturday
Sunday
Monday
Days of the weekend two-valued membership
Thursday
Friday
Saturday
Sunday
Monday
Days of the weekend multivalued membership
Technically, the representation on the right is from the domain of multivalued
logic (or multivalent logic). If I ask the question “Is X a member of set A?” the
answer might be yes, no, or any one of a thousand intermediate values in
between. In other words, X might have partial membership in A. Multivalued
logic stands in direct contrast to the more familiar concept of two-valued (or
bivalent yes-no) logic. Two-valued logic has played a central role in the history
of science since Aristotle first codified it, but the time has come for it to share
the stage.
To return to our example, now consider a continuous scale time plot of
weekend-ness shown below.
2-22
Foundations of Fuzzy Logic
1.0
weekend-ness
weekend-ness
1.0
0.0
0.0
Thursday
Friday
Saturday
Sunday
Monday
Days of the weekend two-valued membership
Thursday
Friday
Saturday
Sunday
Monday
Days of the weekend multivalued membership
By making the plot continuous, we’re defining the degree to which any given
instant belongs in the weekend rather than an entire day. In the plot on the
left, notice that at midnight on Friday, just as the second hand sweeps past 12,
the weekend-ness truth value jumps discontinuously from 0 to 1. This is one
way to define the weekend, and while it may be useful to an accountant, it
doesn’t really connect with our real-world experience of weekend-ness.
The plot on the right shows a smoothly varying curve that accounts for the fact
that all of Friday, and, to a small degree, parts of Thursday, partake of the
quality of weekend-ness and thus deserve partial membership in the fuzzy set
of weekend moments. The curve that defines the weekend-ness of any instant
in time is a function that maps the input space (time of the week) to the output
space (weekend-ness). Specifically it is known as a membership function. We’ll
discuss this in greater detail in the next section.
As another example of fuzzy sets, consider the question of seasons. What
season is it right now? In the northern hemisphere, summer officially begins at
the exact moment in the earth’s orbit when the North Pole is pointed most
directly toward the sun. It occurs exactly once a year, in late June. Using the
astronomical definitions for the season, we get sharp boundaries as shown on
the left in the figure on the next page. But what we experience as the seasons
varies more or less continuously as shown on the right below (in temperate
northern hemisphere climates).
2-23
2
Tutorial
1.0
spring
summer
fall
winter
degree
of
membership
spring
1.0
summer
fall
winter
degree
of
membership
0.0
0.0
March
June
September
December March
March
June
September
December March
Time of the year
Time of the year
Membership Functions
A membership function (MF) is a curve that defines how each point in the input
space is mapped to a membership value (or degree of membership) between 0
and 1. The input space is sometimes referred to as the universe of discourse, a
fancy name for a simple concept.
One of the most commonly used examples of a fuzzy set is the set of tall people.
In this case the universe of discourse is all potential heights, say from 3 feet to
9 feet, and the word “tall” would correspond to a curve that defines the degree
to which any person is tall. If the set of tall people is given the well-defined
(crisp) boundary of a classical set, we might say all people taller than six feet
are officially considered tall. But such a distinction is clearly absurd. It may
make sense to consider the set of all real numbers greater than six because
numbers belong on an abstract plane, but when we want to talk about real
people, it is unreasonable to call one person short and another one tall when
they differ in height by the width of a hair.
excellent!
You must
be taller
than this
line to be
considered
TALL
But if the kind of distinction shown above is unworkable, then what is the right
way to define the set of tall people? Much as with our plot of weekend days, the
2-24
Foundations of Fuzzy Logic
figure below shows a smoothly varying curve that passes from not-tall to tall.
The output-axis is a number known as the membership value between 0 and 1.
The curve is known as a membership function and is often given the
designation of µ. This curve defines the transition from not tall to tall. Both
people are tall to some degree, but one is significantly less tall than the other.
1.0
degree of
membership, µ
tall (µ = 1.0)
sharp-edged
membership
function for
TALL
0.0
not tall (µ = 0.0)
height
1.0
degree of
membership, µ
definitely a tall
person (µ = 0.95)
continuous
membership
function for
TALL
really not very
tall at all (µ = 0.30)
0.0
height
Subjective interpretations and appropriate units are built right into fuzzy sets.
If I say “She’s tall,” the membership function “tall” should already take into
account whether I’m referring to a six-year-old or a grown woman. Similarly,
the units are included in the curve. Certainly it makes no sense to say “Is she
tall in inches or in meters?”
Membership Functions in the Fuzzy Logic Toolbox
The only condition a membership function must really satisfy is that it must
vary between 0 and 1. The function itself can be an arbitrary curve whose
2-25
2
Tutorial
shape we can define as a function that suits us from the point of view of
simplicity, convenience, speed, and efficiency.
A classical set might be expressed as
A = {x | x > 6}
A fuzzy set is an extension of a classical set. If X is the universe of discourse
and its elements are denoted by x, then a fuzzy set A in X is defined as a set of
ordered pairs:
A = {x, µA(x) | x ∈ X}
µA(x) is called the membership function (or MF) of x in A. The membership
function maps each element of X to a membership value between 0 and 1.
The Fuzzy Logic Toolbox includes 11 built-in membership function types.
These 11 functions are, in turn, built from several basic functions: piecewise
linear functions, the Gaussian distribution function, the sigmoid curve, and
quadratic and cubic polynomial curves. For detailed information on any of the
membership functions mentioned below, turn to Chapter 3, “Reference” . By
convention, all membership functions have the letters mf at the end of their
names.
The simplest membership functions are formed using straight lines. Of these,
the simplest is the triangular membership function, and it has the function
name trimf. It’s nothing more than a collection of three points forming a
triangle. The trapezoidal membership function, trapmf, has a flat top and
really is just a truncated triangle curve. These straight line membership
functions have the advantage of simplicity.
1
1
0.75
0.75
0.5
0.5
0.25
0.25
0
0
0
2
4
6
trimf, P = [3 6 8]
trimf
2-26
8
10
0
2
4
6
trapmf, P = [1 5 7 8]
trapmf
8
10
Foundations of Fuzzy Logic
Two membership functions are built on the Gaussian distribution curve: a
simple Gaussian curve and a two-sided composite of two different Gaussian
curves. The two functions are gaussmf and gauss2mf.
The generalized bell membership function is specified by three parameters and
has the function name gbellmf. The bell membership function has one more
parameter than the Gaussian membership function, so it can approach a
non-fuzzy set if the free parameter is tuned. Because of their smoothness and
concise notation, Gaussian and bell membership functions are popular
methods for specifying fuzzy sets. Both of these curves have the advantage of
being smooth and nonzero at all points.
1
1
1
0.75
0.75
0.75
0.5
0.5
0.5
0.25
0.25
0.25
0
0
0
0
2
4
6
gaussmf, P = [2 5]
8
10
0
2
gaussmf
4
6
gauss2mf, P = [1 3 3 4]
8
0
10
2
4
6
gbellmf, P = [2 4 6]
8
10
gbellmf
gauss2mf
Although the Gaussian membership functions and bell membership functions
achieve smoothness, they are unable to specify asymmetric membership
functions, which are important in certain applications. Next we define the
sigmoidal membership function, which is either open left or right. Asymmetric
and closed (i.e. not open to the left or right) membership functions can be
synthesized using two sigmoidal functions, so in addition to the basic sigmf, we
also have the difference between two sigmoidal functions, dsigmf, and the
product of two sigmoidal functions psigmf.
1
1
1
0.75
0.75
0.75
0.5
0.5
0.5
0.25
0.25
0.25
0
0
0
2
4
6
sigmf, P = [2 4]
sigmf
8
10
0
0
2
4
6
dsigmf, P = [5 2 5 7]
dsigmf
8
10
0
2
4
6
psigmf, P = [2 3 −5 8]
8
10
psigmf
Polynomial based curves account for several of the membership functions in
the toolbox. Three related membership functions are the Z, S, and Pi curves, all
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named because of their shape. The function zmf is the asymmetrical
polynomial curve open to the left, smf is the mirror-image function that opens
to the right, and pimf is zero on both extremes with a rise in the middle.
1
1
1
0.75
0.75
0.75
0.5
0.5
0.5
0.25
0.25
0.25
0
0
0
2
4
6
zmf, P = [3 7]
8
10
0
0
2
zmf
4
6
pimf, P = [1 4 5 10]
pimf
8
10
0
2
4
6
smf, P = [1 8]
8
10
smf
There’s a very wide selection to choose from when you’re selecting your favorite
membership function. And the Fuzzy Logic Toolbox also allows you to create
your own membership functions if you find this list too restrictive. On the other
hand, if this list seems bewildering, just remember that you could probably get
along very well with just one or two types of membership functions, for example
the triangle and trapezoid functions. The selection is wide for those who want
to explore the possibilities, but exotic membership functions are by no means
required for perfectly good fuzzy inference systems. Finally, remember that
more details are available on all these functions in the reference section, which
makes up the second half of this manual.
Summary of Membership Functions
• Fuzzy sets describe vague concepts (fast runner, hot weather, weekend
days).
• A fuzzy set admits the possibility of partial membership in it. (Friday is sort
of a weekend day, the weather is rather hot).
• The degree an object belongs to a fuzzy set is denoted by a membership value
between 0 and 1. (Friday is a weekend day to the degree 0.8).
• A membership function associated with a given fuzzy set maps an input
value to its appropriate membership value.
Logical Operations
We now know what’s fuzzy about fuzzy logic, but what about the logic?
The most important thing to realize about fuzzy logical reasoning is the fact
that it is a superset of standard Boolean logic. In other words, if we keep the
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Foundations of Fuzzy Logic
fuzzy values at their extremes of 1 (completely true), and 0 (completely false),
standard logical operations will hold. As an example, consider the standard
truth tables below:
A
B
A and B
A
B
A or B
A
not A
0
0
0
0
0
0
0
1
0
1
0
0
1
1
1
0
1
0
0
1
0
1
1
1
1
1
1
1
AND
OR
NOT
Now remembering that in fuzzy logic the truth of any statement is a matter of
degree, how will these truth tables be altered? The input values can be real
numbers between 0 and 1. What function will preserve the results of the AND
truth table (for example) and also extend to all real numbers between 0 and 1?
One answer is the min operation. That is, resolve the statement A AND B,
where A and B are limited to the range (0,1), by using the function min(A,B).
Using the same reasoning, we can replace the OR operation with the max
function, so that A OR B becomes equivalent to max(A,B). Finally, the
operation NOT A becomes equivalent to the operation 1 – A . Notice how the
truth table above is completely unchanged by this substitution.
A
B
min(A,B)
A
B
max(A,B)
A
1-A
0
0
0
0
0
0
0
1
0
1
0
0
1
1
1
0
1
0
0
1
0
1
1
1
1
1
1
1
AND
OR
NOT
Moreover, since there is a function behind the truth table rather than just the
truth table itself, we can now consider values other than 1 and 0.
The next figure uses a graph to show the same information. We’ve converted
the truth table to a plot of two fuzzy sets applied together to create one fuzzy
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set. The upper part of the figure displays plots corresponding to the two-valued
truth tables above, while the lower part of the figure displays how the
operations work over a continuously varying range of truth values A and B
according to the fuzzy operations we’ve defined.
A
A
A
B
Two-valued
logic
B
A or B
not A
A and B
B
A
B
A
A
Multivalued
logic
not A
A and B
A or B
AND
OR
NOT
min(A,B)
max(A,B)
(1-A)
Given these three functions, we can resolve any construction using fuzzy sets
and the fuzzy logical operation AND, OR, and NOT.
Additional Fuzzy Operators
We’ve only defined here one particular correspondence between two-valued
and multivalued logical operations for AND, OR, and NOT. This
correspondence is by no means unique.
In more general terms, we’re defining what are known as the fuzzy intersection
or conjunction (AND), fuzzy union or disjunction (OR), and fuzzy complement
(NOT). We have defined above what we’ll call the classical operators for these
functions: AND = min, OR = max, and NOT = additive complement. Typically
most fuzzy logic applications make use of these operations and leave it at that.
In general, however, these functions are arbitrary to a surprising degree. The
Fuzzy Logic Toolbox uses the classical operator for the fuzzy complement as
shown above, but also enables you to customize the AND and OR operators.
The intersection of two fuzzy sets A and B is specified in general by a binary
mapping T, which aggregates two membership functions as follows:
µA∩B(x) = T(µA(x), µB(x))
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Foundations of Fuzzy Logic
For example, the binary operator T may represent the multiplication of
µ A ( x ) and µ B ( x ) . These fuzzy intersection operators, which are usually
referred to as T-norm (Triangular norm) operators, meet the following basic
requirements.
A T-norm operator is a binary mapping T( . , . ) satisfying:
boundary: T(0, 0) = 0, T(a, 1) = T(1, a) = a
monotonicity: T(a, b) <= T(c, d) if a <= c and b <= d
commutativity: T(a, b) = T(b, a)
associativity: T(a, T(b, c)) = T(T(a, b), c)
The first requirement imposes the correct generalization to crisp sets. The
second requirement implies that a decrease in the membership values in A or
B cannot produce an increase in the membership value in A intersection B. The
third requirement indicates that the operator is indifferent to the order of the
fuzzy sets to be combined. Finally, the fourth requirement allows us to take the
intersection of any number of sets in any order of pairwise groupings.
Like fuzzy intersection, the fuzzy union operator is specified in general by a
binary mapping S:
µA∪B(x) = S(µA(x), µB(x))
For example, the binary operator S can represent the addition of
µ A ( x ) and µ B ( x ). These fuzzy union operators, which are often referred to as
T-conorm (or S-norm) operators, must satisfy the following basic requirements.
A T-conorm (or S-norm) operator is a binary mapping S( . , . ) satisfying:
boundary: S(1, 1) = 1, S(a, 0) = S(0, a) = a
monotonicity: S(a, b) <= S(c, d) if a <= c and b <= d
commutativity: S(a, b) = S(b, a)
associativity: S(a, S(b, c)) = S(S(a, b), c)
Several parameterized T-norms and dual T-conorms have been proposed in the
past, such as those of Yager [Yag80], Dubois and Prade [Dub80], Schweizer and
Sklar [Sch63], and Sugeno [Sug77]. Each of these provides a way to vary the
“gain” on the function so that it can be very restrictive or very permissive.
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If-Then Rules
Fuzzy sets and fuzzy operators are the subjects and verbs of fuzzy logic. These
if-then rule statements are used to formulate the conditional statements that
comprise fuzzy logic.
A single fuzzy if-then rule assumes the form
if x is A then y is B
where A and B are linguistic values defined by fuzzy sets on the ranges
(universes of discourse) X and Y, respectively. The if-part of the rule “x is A” is
called the antecedent or premise, while the then-part of the rule “y is B” is called
the consequent or conclusion. An example of such a rule might be
if service is good then tip is average
Note that good is represented as a number between 0 and 1, and so the
antecedent is an interpretation that returns a single number between 0 and 1.
On the other hand, average is represented as a fuzzy set, and so the consequent
is an assignment that assigns the entire fuzzy set B to the output variable y. In
the if-then rule, the word “is” gets used in two entirely different ways
depending on whether it appears in the antecedent or the consequent. In
MATLAB terms, this is the distinction between a relational test using “==” and
a variable assignment using the “=” symbol. A less confusing way of writing the
rule would be
if service == good then tip = average
In general, the input to an if-then rule is the current value for the input
variable (in this case, service) and the output is an entire fuzzy set (in this case,
average). This set will later be defuzzified, assigning one value to the output.
The concept of defuzzification is described in the next section, on page 2-41.
Interpreting an if-then rule involves distinct parts: first evaluating the
antecedent (which involves fuzzifying the input and applying any necessary
fuzzy operators) and second applying that result to the consequent (known as
implication). In the case of two-valued or binary logic, if-then rules don’t
present much difficulty. If the premise is true, then the conclusion is true. If we
relax the restrictions of two-valued logic and let the antecedent be a fuzzy
statement, how does this reflect on the conclusion? The answer is a simple one:
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Foundations of Fuzzy Logic
if the antecedent is true to some degree of membership, then the consequent is
also true to that same degree. In other words
in binary logic: p → q (p and q are either both true or both false)
in fuzzy logic: 0.5 p → 0.5 q (partial antecedents provide partial implication)
The antecedent of a rule can have multiple parts:
if sky is gray and wind is strong and barometer is falling, then ...
in which case all parts of the antecedent are calculated simultaneously and
resolved to a single number using the logical operators described in the
preceding section. The consequent of a rule can also have multiple parts:
if temperature is cold then hot water valve is open and cold water valve is shut
in which case all consequents are affected equally by the result of the
antecedent. How is the consequent affected by the antecedent? The consequent
specifies a fuzzy set be assigned to the output. The implication function then
modifies that fuzzy set to the degree specified by the antecedent. The most
common ways to modify the output fuzzy set are truncation using the min
function (where the fuzzy set is “chopped off” as shown below) or scaling using
the prod function (where the output fuzzy set is “squashed”). Both are
supported by the Fuzzy Logic Toolbox, but we use truncation for the examples
in this section.
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Antecedent
If service is excellent or
excellent
1. Fuzzify
inputs
Consequent
food is delicious
then tip = generous
0.7
delicious
0.0
food (crisp)
service (crisp)
µ(food==delicious) = 0 .7
µ(service==excellent) = 0 .0
If
(
0.0
or
0.7
2. Apply
OR operator
(max)
)
then tip = generous
0.7
0.7
0.0
max(0.0, 0.7) = 0.7
If
3. Apply
implication
operator (min)
(
0.7
)
then tip = generous
0.7
generous
min(0.7, generous)
tip (fuzzy)
Summary of If-Then Rules
Interpreting if-then rules is a three-part process. This process is explained in
detail in the next section.
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Foundations of Fuzzy Logic
1 Fuzzify inputs: Resolve all fuzzy statements in the antecedent to a degree of
membership between 0 and 1. If there is only one part to the antecedent, this
is the degree of support for the rule.
2 Apply fuzzy operator to multiple part antecedents: If there are multiple parts
to the antecedent, apply fuzzy logic operators and resolve the antecedent to
a single number between 0 and 1. This is the degree of support for the rule.
3 Apply implication method: Use the degree of support for the entire rule to
shape the output fuzzy set. The consequent of a fuzzy rule assigns an entire
fuzzy set to the output. This fuzzy set is represented by a membership
function that is chosen to indicate the qualities of the consequent. If the
antecedent is only partially true, (i.e., is assigned a value less than 1), then
the output fuzzy set is truncated according to the implication method.
In general, one rule by itself doesn’t do much good. What’s needed are two or
more rules that can play off one another. The output of each rule is a fuzzy set.
The output fuzzy sets for each rule are then aggregated into a single output
fuzzy set. Finally the resulting set is defuzzified, or resolved to a single
number. The next section shows how the whole process works from beginning
to end for a particular type of fuzzy inference system called a Mamdani type.
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Fuzzy Inference Systems
Fuzzy inference is the process of formulating the mapping from a given input
to an output using fuzzy logic. The mapping then provides a basis from which
decisions can be made, or patterns discerned. The process of fuzzy inference
involves all of the pieces that are described in the previous sections:
membership functions, fuzzy logic operators, and if-then rules. There are two
types of fuzzy inference systems that can be implemented in the Fuzzy Logic
Toolbox: Mamdani-type and Sugeno-type. These two types of inference systems
vary somewhat in the way outputs are determined. Descriptions of these two
types of fuzzy inference systems can be found in the references, [Jan97,
Mam75, Sug85].
Fuzzy inference systems have been successfully applied in fields such as
automatic control, data classification, decision analysis, expert systems, and
computer vision. Because of its multidisciplinary nature, fuzzy inference
systems are associated with a number of names, such as fuzzy-rule-based
systems, fuzzy expert systems, fuzzy modeling, fuzzy associative memory,
fuzzy logic controllers, and simply (and ambiguously) fuzzy systems. Since the
terms used to describe the various parts of the fuzzy inference process are far
from standard, we will try to be as clear as possible about the different terms
introduced in this section.
Mamdani’s fuzzy inference method is the most commonly seen fuzzy
methodology. Mamdani’s method was among the first control systems built
using fuzzy set theory. It was proposed in 1975 by Ebrahim Mamdani [Mam75]
as an attempt to control a steam engine and boiler combination by synthesizing
a set of linguistic control rules obtained from experienced human operators.
Mamdani’s effort was based on Lotfi Zadeh’s 1973 paper on fuzzy algorithms
for complex systems and decision processes [Zad73]. Although the inference
process we describe in the next few sections differs somewhat from the methods
described in the original paper, the basic idea is much the same.
Mamdani-type inference, as we have defined it for the Fuzzy Logic Toolbox,
expects the output membership functions to be fuzzy sets. After the
aggregation process, there is a fuzzy set for each output variable that needs
defuzzification. It’s possible, and in many cases much more efficient, to use a
single spike as the output membership function rather than a distributed fuzzy
set. This is sometimes known as a singleton output membership function, and
it can be thought of as a pre-defuzzified fuzzy set. It enhances the efficiency of
the defuzzification process because it greatly simplifies the computation
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Fuzzy Inference Systems
required by the more general Mamdani method, which finds the centroid of a
two-dimensional function. Rather than integrating across the two-dimensional
function to find the centroid, we use the weighted average of a few data points.
Sugeno-type systems support this type of model. In general, Sugeno-type
systems can be used to model any inference system in which the output
membership functions are either linear or constant.
Dinner for Two, Reprise
In this section we provide the same two-input one-output three-rule tipping
problem that you saw in the introduction, only in more detail. The basic
structure of this example is shown in the diagram below.
Dinner for two
a 2 input, 1 output, 3 rule system
Rule 1
If service is poor or food is rancid,
then tip is cheap.
Rule 2
If service is good, then tip is average.
Rule 3
If service is excellent or food is delicious,
then tip is generous.
Input 1
Service (0-10)
Input 2
Food (0-10)
The inputs are crisp
(non-fuzzy) numbers
limited to a specific
range.
All rules are
evaluated in parallel
using fuzzy
reasoning.
Σ
The results of the rules
are combined and
distilled (defuzzified).
Output
Tip (5-25%)
The result is a crisp
(non-fuzzy) number.
Information flows from left to right, from two inputs to a single output. The
parallel nature of the rules is one of the more important aspects of fuzzy logic
systems. Instead of sharp switching between modes based on breakpoints, we
will glide smoothly from regions where the system’s behavior is dominated by
either one rule or another.
In the Fuzzy Logic Toolbox, there are five parts of the fuzzy inference process:
fuzzification of the input variables, application of the fuzzy operator (AND or
OR) in the antecedent, implication from the antecedent to the consequent,
aggregation of the consequents across the rules, and defuzzification. These
sometimes cryptic and odd names have very specific meaning that we’ll define
carefully as we step through each of them in more detail below.
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Step 1. Fuzzify Inputs
The first step is to take the inputs and determine the degree to which they
belong to each of the appropriate fuzzy sets via membership functions. In the
Fuzzy Logic Toolbox, the input is always a crisp numerical value limited to the
universe of discourse of the input variable (in this case the interval between 0
and 10) and the output is a fuzzy degree of membership in the qualifying
linguistic set (always the interval between 0 and 1). Fuzzification of the input
amounts to either a table lookup or a function evaluation.
The example we’re using in this section is built on three rules, and each of the
rules depends on resolving the inputs into a number of different fuzzy linguistic
sets: service is poor, service is good, food is rancid, food is delicious, and so on.
Before the rules can be evaluated, the inputs must be fuzzified according to
each of these linguistic sets. For example, to what extent is the food really
delicious? The figure below shows how well the food at our hypothetical
restaurant (rated on a scale of 0 to 10) qualifies, (via its membership function),
as the linguistic variable “delicious.” In this case, we rated the food as an 8,
which, given our graphical definition of delicious, corresponds to µ = 0.7 for the
“delicious” membership function.
1. Fuzzify
inputs.
0.7
delicious
Result of
fuzzification
food is delicious
food = 8
input
(The compliment to the chef would be “your food is delicious to the degree 0.7.”)
In this manner, each input is fuzzified over all the qualifying membership
functions required by the rules.
Step 2. Apply Fuzzy Operator
Once the inputs have been fuzzified, we know the degree to which each part of
the antecedent has been satisfied for each rule. If the antecedent of a given rule
has more than one part, the fuzzy operator is applied to obtain one number that
represents the result of the antecedent for that rule. This number will then be
applied to the output function. The input to the fuzzy operator is two or more
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Fuzzy Inference Systems
membership values from fuzzified input variables. The output is a single truth
value.
As is described in the section on fuzzy logical operations, any number of
well-defined methods can fill in for the AND operation or the OR operation. In
the Fuzzy Logic Toolbox, two built-in AND methods are supported: min
(minimum) and prod (product). Two built-in OR methods are also supported:
max (maximum), and the probabilistic OR method probor. The probabilistic OR
method (also known as the algebraic sum) is calculated according to the
equation
probor(a,b) = a + b - ab
In addition to these built-in methods, you can create your own methods for
AND and OR by writing any function and setting that to be your method of
choice. There will be more information on how to do this later.
Shown below is an example of the OR operator max at work. We’re evaluating
the antecedent of the rule 3 for the tipping calculation. The two different pieces
of the antecedent (service is excellent and food is delicious) yielded the fuzzy
membership values 0.0 and 0.7 respectively. The fuzzy OR operator simply
selects the maximum of the two values, 0.7, and the fuzzy operation for rule 3
is complete. If we were using the probabilistic OR method, the result would still
be 0.7 in this case.
1. Fuzzify
inputs.
2. Apply
OR operator (max).
excellent
0.7
0.7
0.0
service is excellent
or
delicious
0.0
result of
fuzzy operator
food is delicious
service = 3
food = 8
input 1
input 2
Step 3. Apply Implication Method
Before applying the implication method, we must take care of the rule’s weight.
Every rule has a weight (a number between 0 and 1), which is applied to the
number given by the antecedent. Generally this weight is 1 (as it is for this
example) and so it has no effect at all on the implication process. From time to
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time you may want to weight one rule relative to the others by changing its
weight value to something other than 1.
Once proper weighting has been assigned to each rule, the implication method
is implemented. A consequent is a fuzzy set represented by a membership
function, which weights appropriately the linguistic characteristics that are
attributed to it. The consequent is reshaped using a function associated with
the antecedent (a single number). The input for the implication process is a
single number given by the antecedent, and the output is a fuzzy set.
Implication is implemented for each rule. Two built-in methods are supported,
and they are the same functions that are used by the AND method: min
(minimum), which truncates the output fuzzy set, and prod (product), which
scales the output fuzzy set.
Antecedent
Consequent
2. Apply
OR operator (max).
1. Fuzzify
inputs.
3. Apply
Implication
operator (min).
excellent
generous
delicious
If service is excellent
or
food is delicious
service = 3
food = 8
input 1
input 2
then
tip = generous
result of
implication
Step 4. Aggregate All Outputs
Since decisions are based on the testing of all of the rules in an FIS, the rules
must be combined in some manner in order to make a decision. Aggregation is
the process by which the fuzzy sets that represent the outputs of each rule are
combined into a single fuzzy set. Aggregation only occurs once for each output
variable, just prior to the fifth and final step, defuzzification. The input of the
aggregation process is the list of truncated output functions returned by the
implication process for each rule. The output of the aggregation process is one
fuzzy set for each output variable.
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Fuzzy Inference Systems
Notice that as long as the aggregation method is commutative (which it always
should be), then the order in which the rules are executed is unimportant.
Three built-in methods are supported: max (maximum), probor (probabilistic
or), and sum (simply the sum of each rule’s output set).
In the diagram below, all three rules have been placed together to show how
the output of each rule is combined, or aggregated, into a single fuzzy set whose
membership function assigns a weighting for every output (tip) value.
1.
poor
3. Apply
implication
method (min).
2. Apply
fuzzy
operation
(OR = max).
1. Fuzzify inputs.
cheap
rancid
0
If
service is poor
or
food is rancid
then
25%
tip = cheap
0
25%
0
25%
0
25%
average
2.
rule 2 has
no dependency
on input 2
good
0
If
3.
service is good
then
25%
tip = average
excellent
generous
delicious
0
If service is excellent
or
food is delicious
service = 3
food = 8
input 1
input 2
then
25%
tip = generous
0
4. Apply
aggregation
method (max).
25%
Result of
aggregation
Step 5. Defuzzify
The input for the defuzzification process is a fuzzy set (the aggregate output
fuzzy set) and the output is a single number. As much as fuzziness helps the
rule evaluation during the intermediate steps, the final desired output for each
variable is generally a single number. However, the aggregate of a fuzzy set
encompasses a range of output values, and so must be defuzzified in order to
resolve a single output value from the set.
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Perhaps the most popular defuzzification method is the centroid calculation,
which returns the center of area under the curve. There are five built-in
methods supported: centroid, bisector, middle of maximum (the average of the
maximum value of the output set), largest of maximum, and smallest of
maximum.
0
25%
5. Defuzzify the
aggregate output
(centroid).
tip = 16.7%
Result of
defuzzification
The Fuzzy Inference Diagram
The fuzzy inference diagram is the composite of all the smaller diagrams we’ve
been looking at so far in this section. It simultaneously displays all parts of the
fuzzy inference process we’ve examined. Information flows through the fuzzy
inference diagram as shown below.
Interpreting the
Fuzzy Inference
Diagram
1. if
and
then
2. if
and
then
input 1
input 2
output
Notice how the flow proceeds up from the inputs in the lower left, then across
each row, or rule, and then down the rule outputs to finish in the lower right.
This is a very compact way of showing everything at once, from linguistic
variable fuzzification all the way through defuzzification of the aggregate
output.
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Fuzzy Inference Systems
Shown below is the real full-size fuzzy inference diagram. There’s a lot to see
in a fuzzy inference diagram, but once you become accustomed to it, you can
learn a lot about a system very quickly. For instance, from this diagram with
these particular inputs, you can easily see that the implication method is
truncation with the min function. The max function is being used for the fuzzy
OR operation. Rule 3 (the bottom-most row in the diagram shown opposite) is
having the strongest influence on the output. And so on. The Rule Viewer
described in “The Rule Viewer” on page 2-59 is a MATLAB implementation of
the fuzzy inference diagram.
2. Apply
fuzzy
operation
(OR = max).
1. Fuzzify inputs.
1.
poor
rancid
0
If
3. Apply
implication
method (min).
0
10
service is poor
or
cheap
0%
10
food is rancid
25%
then
0%
25%
0%
25%
0%
25% 4. Apply
tip = cheap
average
2.
rule 2 has
no dependency
on input 2
good
0
If
25%
then
tip = average
excellent
3.
generous
delicious
0
If
0%
10
service is good
10
service is excellent
0
or
0%
10
food is delicious
service = 3
food = 8
input 1
input 2
then
25%
aggregation
method (max).
tip = generous
tip = 16.7%
0%
25%
5. Defuzzify
(centroid).
output
Customization
One of the primary goals of the Fuzzy Logic Toolbox is to have an open and
easily modified fuzzy inference system structure. Thus, the Fuzzy Logic
Toolbox is designed to give you as much freedom as possible, within the basic
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constraints of the process described here, to customize the fuzzy inference
process for your application. For example, you can substitute your own
MATLAB functions for any of the default functions used in the five steps
detailed above: you make your own membership functions, AND methods, OR
methods, implication methods, aggregation methods, and defuzzification
methods. The next section describes exactly how to build and implement a
fuzzy inference system using the tools provided.
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Building Systems with the Fuzzy Logic Toolbox
Building Systems with the Fuzzy Logic Toolbox
Dinner for Two, from the Top
Now we’re going to work through a similar tipping example, only we’ll be
building it using the graphical user interface (GUI) tools provided by the Fuzzy
Logic Toolbox. Although it’s possible to use the Fuzzy Logic Toolbox by working
strictly from the command line, in general it’s much easier to build a system
graphically. There are five primary GUI tools for building, editing, and
observing fuzzy inference systems in the Fuzzy Logic Toolbox: the Fuzzy
Inference System or FIS Editor, the Membership Function Editor, the Rule
Editor, the Rule Viewer, and the Surface Viewer. These GUIs are dynamically
linked, in that changes you make to the FIS using one of them, can affect what
you see on any of the other open GUIs. You can have any or all of them open for
any given system.
In addition to these five primary GUIs, the toolbox includes the graphical
ANFIS Editor GUI, which is used for building and analyzing Sugeno-type
adaptive neural fuzzy inference systems. The ANFIS Editor GUI is discussed
later in this chapter, in the section, “Sugeno-Type Fuzzy Inference” on page
2-86.
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FIS Editor
Membership
Function Editor
Rule Editor
Fuzzy
Inference
System
Read-only
tools
Rule Viewer
Surface Viewer
The FIS Editor handles the high level issues for the system: How many input
and output variables? What are their names? The Fuzzy Logic Toolbox doesn’t
limit the number of inputs. However, the number of inputs may be limited by
the available memory of your machine. If the number of inputs is too large, or
the number of membership functions is too big, then it may also be difficult to
analyze the FIS using the other GUI tools.
The Membership Function Editor is used to define the shapes of all the
membership functions associated with each variable.
The Rule Editor is for editing the list of rules that defines the behavior of the
system.
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The Rule Viewer and the Surface Viewer are used for looking at, as opposed to
editing, the FIS. They are strictly read-only tools. The Rule Viewer is a
MATLAB-based display of the fuzzy inference diagram shown at the end of the
last section. Used as a diagnostic, it can show (for example) which rules are
active, or how individual membership function shapes are influencing the
results. The Surface Viewer is used to display the dependency of one of the
outputs on any one or two of the inputs—that is, it generates and plots an
output surface map for the system.
This chapter began with an illustration similar to the one below describing the
main parts of a fuzzy inference system, only the one below shows how the three
Editors fit together. The two Viewers examine the behavior of the entire
system.
The General Case...
A Specific Example...
Input
service
Output
tip
if service is poor then tip is cheap
if service is good then tip is average
if service is excellent then tip is generous
Rules
Input
terms
Output
terms
(interpret)
(assign)
service =
tip =
{poor,
good,
excellent}
{cheap,
average,
generous}
The GUI Editors...
The FIS Editor
The Rule Editor
The Membership
Function Editor
The five primary GUIs can all interact and exchange information. Any one of
them can read and write both to the workspace and to the disk (the read-only
viewers can still exchange plots with the workspace and/or the disk). For any
fuzzy inference system, any or all of these five GUIs may be open. If more than
one of these editors is open for a single system, the various GUI windows are
aware of the existence of the others, and will, if necessary, update related
windows. Thus if the names of the membership functions are changed using
the Membership Function Editor, those changes are reflected in the rules
shown in the Rule Editor. The editors for any number of different FIS systems
may be open simultaneously. The FIS Editor, the Membership Function
Editor, and the Rule Editor can all read and modify the FIS data, but the Rule
Viewer and the Surface Viewer do not modify the FIS data in any way.
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Getting Started
We’ll start with a basic description of a two-input, one-output tipping problem
(based on tipping practices in the U.S.).
The Basic Tipping Problem. Given a number between 0 and 10 that represents the
quality of service at a restaurant (where 10 is excellent), and another number
between 0 and 10 that represents the quality of the food at that restaurant
(again, 10 is excellent), what should the tip be?
The starting point is to write down the three golden rules of tipping, based on
years of personal experience in restaurants.
1. If the service is poor or the food is rancid, then tip is cheap.
2. If the service is good, then tip is average.
3. If the service is excellent or the food is delicious, then tip is generous.
We’ll assume that an average tip is 15%, a generous tip is 25%, and a cheap tip
is 5%. It’s also useful to have a vague idea of what the tipping function should
look like.
.25
.15
.5
Bad service or bad food
Great service or great food
Obviously the numbers and the shape of the curve are subject to local
traditions, cultural bias, and so on, but the three rules are pretty universal.
Now we know the rules, and we have an idea of what the output should look
like. Let’s begin working with the GUI tools to construct a fuzzy inference
system for this decision process.
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The FIS Editor
These menu items allow you to save, open,
or edit a fuzzy system using any of the five
basic GUI tools.
Double-click on an input
variable icon to open the
Membership Function Editor.
Double-click on the system
diagram to open the
Rule Editor.
The name of the system is displayed
here. It can be changed using one of the
Save as... menu options.
Double-click on the icon for
the output variable, tip, to
open the Membership
Function Editor.
These pop-up menus are used to
adjust the fuzzy inference functions,
such as the defuzzification method.
This edit field is used to name
and edit the names of the
input and output variable.
This status line describes the
most recent operation.
The following discussion walks you through building a new fuzzy inference
system from scratch. If you want to save time and follow along quickly, you can
load the already built system by typing
fuzzy tipper
This will load the FIS associated with the file tipper.fis (the .fis is implied)
and launch the FIS Editor. However, if you load the pre-built system, you will
not be building rules and constructing membership functions.
The FIS Editor displays general information about a fuzzy inference system.
There’s a simple diagram at the top that shows the names of each input
variable on the left, and those of each output variable on the right. The sample
membership functions shown in the boxes are just icons and do not depict the
actual shapes of the membership functions.
Below the diagram is the name of the system and the type of inference used.
The default, Mamdani-type inference, is what we’ve been describing so far and
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what we’ll continue to use for this example. Another slightly different type of
inference, called Sugeno-type inference, is also available. This method is
explained in “Sugeno-Type Fuzzy Inference” on page 2-86. Below the name of
the fuzzy inference system, on the left side of the figure, are the pop-up menus
that allow you to modify the various pieces of the inference process. On the
right side at the bottom of the figure is the area that displays the name of either
an input or output variable, its associated membership function type, and its
range. The latter two fields are specified only after the membership functions
have been. Below that region are the Help and Close buttons that call up
online help and close the window, respectively. At the bottom is a status line
that relays information about the system.
To start this system from scratch, type
fuzzy
at the MATLAB prompt. The generic untitled FIS Editor opens, with one input,
labeled input1, and one output, labeled output1. For this example, we will
construct a two-input, one output system, so go to the Edit menu and select
Add input. A second yellow box labeled input2 will appear. The two inputs we
will have in our example are service and food. Our one output is tip. We’d like
to change the variable names to reflect that, though.
1 Click once on the left-hand (yellow) box marked input1 (the box will be
highlighted in red).
2 In the white edit field on the right, change input1 to service and press
Return.
3 Click once on the left-hand (yellow) box marked input2 (the box will be
highlighted in red).
4 In the white edit field on the right, change input2 to food and press Return.
5 Click once on the right-hand (blue) box marked output1.
6 In the white edit field on the right, change output1 to tip.
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7 From the File menu select Save to workspace as....
8 Enter the variable name tipper and click on OK.
You will see the diagram updated to reflect the new names of the input and
output variables. There is now a new variable in the workspace called tipper
that contains all the information about this system. By saving to the workspace
with a new name, you also rename the entire system. Your window will look
something like this.
Leave the inference options in the lower left in their default positions for now.
You’ve entered all the information you need for this particular GUI. Next
define the membership functions associated with each of the variables. To do
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this, open the Membership Function Editor. You can open the Membership
Function Editor in one of three ways:
• Pull down the View menu item and select Edit Membership Functions....
• Double-click on the icon for the output variable, tip.
• Type mfedit at the command line.
The Membership Function Editor
These menu items allow you
to save, open, or edit a fuzzy
system using any of the five
basic GUI tools.
These text fields display
the name and type of
the current variable.
This edit field lets
you set the range of
the current variable.
This edit field lets you set
the display range of the
current plot.
This status line describes
the most recent operation.
This is the “Variable Palette”
area. Click on a variable here
to make it current and edit its
membership functions.
This graph field displays all
the membership functions
of the current variable.
Click on a line to select it and you
can change any of its attributes,
including name, type and
numerical parameters. Drag your
mouse to move or change the shape
of a selected membership function.
This edit field lets you
change the name of the
current membership
function.
This pop-up menu lets
you change the type
of the current
membership function.
This edit field lets
you change the
numerical
parameters for the
current membership
function.
The Membership Function Editor shares some features with the FIS Editor. In
fact, all of the five basic GUI tools have similar menu options, status lines, and
Help and Close buttons. The Membership Function Editor is the tool that lets
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you display and edit all of the membership functions associated with all of the
input and output variables for the entire fuzzy inference system.
When you open the Membership Function Editor to work on a fuzzy inference
system that does not already exist in the workspace, there are not yet any
membership functions associated with the variables that you have just defined
with the FIS Editor.
On the upper left side of the graph area in the Membership Function Editor is
a “Variable Palette” that lets you set the membership functions for a given
variable.To set up your membership functions associated with an input or an
output variable for the FIS, select an FIS variable in this region by clicking on
it.
Next select the Edit pull-down menu, and choose Add MFs.... A new window
will appear, which allows you to select both the membership function type and
the number of membership functions associated with the selected variable. In
the lower right corner of the window are the controls that let you change the
name, type, and parameters (shape), of the membership function, once it has
been selected.
The membership functions from the current variable are displayed in the main
graph. These membership functions can be manipulated in two ways. You can
first use the mouse to select a particular membership function associated with
a given variable quality, (such as poor, for the variable, service), and then drag
the membership function from side to side. This will affect the mathematical
description of the quality associated with that membership function for a given
variable. The selected membership function can also be tagged for dilation or
contraction by clicking on the small square drag points on the membership
function, and then dragging the function with the mouse toward the outside,
for dilation, or toward the inside, for contraction. This will change the
parameters associated with that membership function.
Below the Variable Palette is some information about the type and name of the
current variable. There is a text field in this region that lets you change the
limits of the current variable’s range (universe of discourse) and another that
lets you set the limits of the current plot (which has no real effect on the
system).
The process of specifying the input membership functions for this two input
tipper problem is as follows:
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1 Select the input variable, service, by double-clicking on it. Set both the
Range and the Display Range to the vector [0 10].
2 Select Add MFs... from the Edit menu. The window below pops open.
3 Use the pull-down tab to choose gaussmf for MF Type and 3 for Number of
MFs. This adds three Gaussian curves to the input variable service
4 Click once on the curve with the leftmost hump. Change the name of the
curve to poor. To adjust the shape of the membership function, either use
the mouse, as described above, or type in a desired parameter change, and
then click on the membership function. The default parameter listing for
this curve is [1.5 0].
5 Name the curve with the middle hump, good, and the curve with the
rightmost hump, excellent. Reset the associated parameters if desired.
6 Select the input variable, food, by clicking on it. Set both the Range and the
Display Range to the vector [0 10].
7 Select Add MFs... from the Edit menu and add two trapmf curves to the
input variable food.
8 Click once directly on the curve with the leftmost trapazoid. Change the
name of the curve to rancid. To adjust the shape of the membership
function, either use the mouse, as described above, or type in a desired
parameter change, and then click on the membership function. The default
parameter listing for this curve is [0 0 1 3].
9 Name the curve with the rightmost trapazoid, delicious, and reset the
associated parameters if desired
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Next you need to create the membership functions for the output variable, tip.
To create the output variable membership functions, use the Variable Palette
on the left, selecting the output variable, tip. The inputs ranged from 0 to 10,
but the output scale is going to be a tip between 5 and 25 percent.
Use triangular membership function types for the output. First, set the Range
(and the Display Range) to [0 30], to cover the output range. Initially, the
cheap membership function will have the parameters [0 5 10], the average
membership function will be [10 15 20], and the generous membership
function will be [20 25 30]. Your system should look something like this.
Now that the variables have been named, and the membership functions have
appropriate shapes and names, you’re ready to write down the rules. To call up
the Rule Editor, go to the View menu and select Edit rules..., or type ruleedit
at the command line.
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The Rule Editor
Input or output selection menus.
The menu items allow
you to save, open, or
edit a fuzzy system
using any of the five
basic GUI tools.
The rules are
entered
automatically
using the GUI
tools .
Link input
statements in rules.
The Help button
gives some
information about
how the Rule
Editor works, and
the Close button
closes the window.
This status line
describes the most
recent operation.
Negate input or output
statements in rules.
Create or edit rules with the GUI buttons and
choices from the input or output selection menus.
Constructing rules using the graphical Rule Editor interface is fairly
self-evident. Based on the descriptions of the input and output variables
defined with the FIS Editor, the Rule Editor allows you to construct the rule
statements automatically, by clicking on and selecting one item in each input
variable box, one item in each output box, and one connection item. Choosing
none as one of the variable qualities will exclude that variable from a given
rule. Choosing not under any variable name will negate the associated quality.
Rules may be changed, deleted, or added, by clicking on the appropriate button.
The Rule Editor also has some familiar landmarks, similar to those in the FIS
Editor and the Membership Function Editor, including the menu bar and the
status line. The Format pop-up menu is available from the Options pull-down
menu from the top menu bar—this is used to set the format for the display.
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Similarly, Language can be set from under Options as well. The Help button
will bring up a MATLAB Help window.
To insert the first rule in the Rule Editor, select the following:
• poor under the variable service
• rancid under the variable food
• the radio button, or, in the Connection block
• cheap, under the output variable, tip.
The resulting rule is:
1. If (service is poor) or (food is rancid) then (tip is cheap) (1)
The numbers in the parentheses represent weights that can be applied to each
rule if desired. You can specify the weights by typing in a desired number
between zero and one under the Weight: setting. If you do not specify them, the
weights are assumed to be unity (1).
Follow a similar procedure to insert the second and third rules in the Rule
Editor to get:
1. If (service is poor) or (food is rancid) then (tip is cheap) (1)
2. If (service is good) then (tip is average) (1)
3. If (service is excellent) or (food is delicious) then (tip is generous) (1)
To change a rule, first click on the rule to be changed. Next make the desired
changes to that rule, and then click on Change rule. For example, to change
the first rule to
1. If (service not poor) or (food not rancid) then (tip is not cheap) (1)
click not under each variable, and then click Change rule.
The Format pop-up menu from the Options menu indicates that you’re looking
at the verbose form of the rules. Try changing it to symbolic. You will see
1. (service==poor) => (tip=cheap) (1)
2. (service==good) => (tip=average) (1)
3. (service==excellent) => (tip=generous) (1)
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There is not much difference in the display really, but it’s slightly more
language neutral, since it doesn’t depend on terms like “if” and “then.” If you
change the format to indexed, you’ll see an extremely compressed version of the
rules that has squeezed all the language out.
1, 1 (1) : 1
2, 2 (1) : 1
3, 3 (1) : 1
This is the version that the machine deals with. The first column in this
structure corresponds to the input variable, the second column corresponds to
the output variable, the third column displays the weight applied to each rule,
and the fourth column is shorthand that indicates whether this is an OR (2)
rule or an AND (1) rule. The numbers in the first two columns refer to the index
number of the membership function. A literal interpretation of rule 1 is: “if
input 1 is MF1 (the first membership function associated with input 1) then
output 1 should be MF1 (the first membership function associated with output
1) with the weight 1.” Since there is only one input for this system, the AND
connective implied by the 1 in the last column is of no consequence.
The symbolic format doesn’t bother with the terms, if, then, and so on. The
indexed format doesn’t even bother with the names of your variables.
Obviously the functionality of your system doesn’t depend on how well you
have named your variables and membership functions. The whole point of
naming variables descriptively is, as always, making the system easier for you
to interpret. Thus, unless you have some special purpose in mind, it will
probably be easier for you to stick with the verbose format.
At this point, the fuzzy inference system has been completely defined, in that
the variables, membership functions, and the rules necessary to calculate tips
are in place. It would be nice, at this point, to look at a fuzzy inference diagram
like the one presented at the end of the previous section and verify that
everything is behaving the way we think it should. This is exactly the purpose
of the Rule Viewer, the next of the GUI tools we’ll look at. From the View menu,
select View rules....
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The Rule Viewer
The menu items allow
you to save, open, or edit
a fuzzy system using any
of the five basic GUI
tools.
Each column of plots (yellow) shows how
the input variable is used in the rules. The
input values are shown here at the top.
Each row of plots
represents one rule (here
there are 3). Click on a
rule to display it in the
status bar.
This column of plots (blue)
shows how the output variable
is used in the rules.
This line provides a
defuzzified value.
Slide this line to change
your input values, and
generate a new output
response.
The bottom-right plot
shows how the output of
each rule is combined to
make an aggregate
output and then
defuzzified.
This edit field allows you
to set the input
explicitly.
Shift the plots left, right,
up, or down with these
buttons.
This status line describes the most recent operation.
The Rule Viewer displays a roadmap of the whole fuzzy inference process. It’s
based on the fuzzy inference diagram described in the previous section. You see
a single figure window with 10 small plots nested in it. The three small plots
across the top of the figure represent the antecedent and consequent of the first
rule. Each rule is a row of plots, and each column is a variable. The first two
columns of plots (the six yellow plots) show the membership functions
referenced by the antecedent, or the if-part of each rule. The third column of
plots (the three blue plots) shows the membership functions referenced by the
consequent, or the then-part of each rule. If you click once on a rule number,
the corresponding rule will be displayed at the bottom of the figure. Notice that
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under food, there is a plot which is blank. This corresponds to the
characterization of none for the variable food in the second rule. The fourth
plot in the third column of plots represents the aggregate weighted decision for
the given inference system. This decision will depend on the input values for
the system.
There are also the now familiar items like the status line and the menu bar. In
the lower right there is a text field into which you can enter specific input
values. For the two-input system, you will enter an input vector, [9 8], for
example, and then click on input. You can also adjust these input values by
clicking anywhere on any of the three plots for each input. This will move the
red index line horizontally, to the point where you have clicked. You can also
just click and drag this line in order to change the input values. When you
release the line, (or after manually specifying the input), a new calculation is
performed, and you can see the whole fuzzy inference process take place.
Where the index line representing service crosses the membership function
line “service is poor” in the upper left plot will determine the degree to which
rule one is activated. A yellow patch of color under the actual membership
function curve is used to make the fuzzy membership value visually apparent.
Each of the characterizations of each of the variables is specified with respect
to the input index line in this manner. If we follow rule 1 across the top of the
diagram, we can see the consequent “tip is cheap” has been truncated to exactly
the same degree as the (composite) antecedent—this is the implication process
in action. The aggregation occurs down the third column, and the resultant
aggregate plot is shown in the single plot to be found in the lower right corner
of the plot field. The defuzzified output value is shown by the thick line passing
through the aggregate fuzzy set.
The Rule Viewer allows you to interpret the entire fuzzy inference process at
once. The Rule Viewer also shows how the shape of certain membership
functions influences the overall result. Since it plots every part of every rule, it
can become unwieldy for particularly large systems, but, for a relatively small
number of inputs and outputs, it performs well (depending on how much screen
space you devote to it) with up to 30 rules and as many as 6 or 7 variables.
The Rule Viewer shows one calculation at a time and in great detail. In this
sense, it presents a sort of micro view of the fuzzy inference system. If you want
to see the entire output surface of your system, that is, the entire span of the
output set based on the entire span of the input set, you need to open up the
Surface Viewer. This is the last of our five basic GUI tools in the Fuzzy Logic
Toolbox, and you open it by selecting View surface... from the View menu.
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The Surface Viewer
Use mouse to rotate the axes.
The menu items allow
you to save, open, or
edit a fuzzy system
using any of the five
basic GUI tools.
This plot shows the
output surface for any
output of the system
versus any one or two
inputs to the system
These pop-up menus
let you specify the
one or two displayed
input variables.
This pop-up menu lets
you specify the
displayed output
variable.
These edit fields let
you determine how
densely to grid the
input space.
Push this button when
you’re ready to
calculate and plot.
This edit field lets you
set the input explicitly
for inputs not specified
in the surface plot.
This status line describes the most recent operation.
The Help button gives
some information
about how the Surface
Viewer works, and the
Close button closes the
window.
Upon opening the Surface Viewer, we are presented with a two-dimensional
curve that represents the mapping from service quality to tip amount. Since
this is a one-input one-output case, we can see the entire mapping in one plot.
Two-input one-output systems also work well, as they generate
three-dimensional plots that MATLAB can adeptly manage. When we move
beyond three dimensions overall, we start to encounter trouble displaying the
results. Accordingly, the Surface Viewer is equipped with pop-up menus that
let you select any two inputs and any one output for plotting. Just below the
pop-up menus are two text input fields that let you determine how many x-axis
and y-axis grid lines you want to include. This allows you to keep the
calculation time reasonable for complex problems. Pushing the Evaluate
button initiates the calculation, and the plot comes up soon after the
calculation is complete. To change the x-axis or y-axis grid after the surface is
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in view, simply change the appropriate text field, and click on either X-grids:
or Y-grids:, according to which text field you changed, to redraw the plot.
The Surface Viewer has a special capability that is very helpful in cases with
two (or more) inputs and one output: you can actually grab the axes and
reposition them to get a different three-dimensional view on the data. The Ref.
Input: field is used in situations when there are more inputs required by the
system than the surface is mapping. Suppose you have a four-input one-output
system and would like to see the output surface. The Surface Viewer can
generate a three-dimensional output surface where any two of the inputs vary,
but two of the inputs must be held constant since computer monitors cannot
display a five-dimensional shape. In such a case the input would be a
four-dimensional vector with NaNs holding the place of the varying inputs while
numerical values would indicate those values that remain fixed. An NaN is the
IEEE symbol for “not a number.”
This concludes the quick walk-through of each of the main GUI tools. Notice
that for the tipping problem, the output of the fuzzy system matches our
original idea of the shape of the fuzzy mapping from service to tip fairly well.
In hindsight, you might say, “Why bother? I could have just drawn a quick
lookup table and been done an hour ago!” However, if you are interested in
solving an entire class of similar decision-making problems, fuzzy logic may
provide an appropriate tool for the solution, given its ease with which a system
can be quickly modified.
Importing and Exporting from the GUI Tools
When you save a fuzzy system to disk, you’re saving an ASCII text FIS file
representation of that system with the file suffix .fis. This text file can be
edited and modified and is simple to understand. When you save your fuzzy
system to the MATLAB workspace, you’re creating a variable (whose name you
choose) that will act as a MATLAB structure for the FIS system. FIS files and
FIS structures represent the same system.
Note: If you do not save your FIS to your disk, but only save it to the
MATLAB workspace, you will not be able to recover it for use in a new
MATLAB session.
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Customizing Your Fuzzy System
If you want to include customized functions as part of your use of the Fuzzy
Logic Toolbox, there are a few guidelines you need to follow. The AND method,
OR method, aggregation method, and defuzzification method functions you
provide need to work in a similar way to max, min, or prod in MATLAB. That is,
they must be able to operate down the columns of a matrix. For example, the
implication method does an element by element matrix operation, similar to
the min function, as in
a=[1 2; 3
b=[2 2; 2
min(a,b)
ans =
1
2
4];
2];
2
2
Custom Membership Functions
You can create your own membership functions using an M-file. The values
these functions can take must be between 0 and 1. There is a limitation on
customized membership functions in that they cannot use more than 16
parameters.
To define a custom membership function named custmf:
1 Create an M-file for a function, custmf.m, that takes values between 0 and
1, and depends on at most 16 parameters.
2 Choose the Add Custom MF item in the Edit menu on the Membership
Function Editor GUI.
3 Enter your custom membership function M-file name, custmf, in the M-file
function name text box.
4 Enter the vector of parameters you want to use to parameterize your
customized membership function in the text box next to Parameter list.
5 Give the custom membership function a name different from any other
membership function name you will use in your FIS.
6 Select OK.
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Here is some sample code for a custom membership function, testmf1, that
depends on eight parameters between 0 and 10.
function out = testmf1(x, params)
for i=1:length(x)
if x(i)<params(1)
y(i)=params(1);
elseif x(i)<params(2)
y(i)=params(2);
elseif x(i)<params(3)
y(i)=params(3);
elseif x(i)<params(4)
y(i)=params(4);
elseif x(i)<params(5)
y(i)=params(5);
elseif x(i)<params(6)
y(i)=params(6);
elseif x(i)<params(7)
y(i)=params(7);
elseif x(i)<params(8)
y(i)=params(8);
else
y(i)=0;
end
end
out=.1*y';
You can try naming this file testmf1.m and loading it into the Membership
Function Editor using the parameters of your choice.
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Working from the Command Line
The tipping example system is one of many example fuzzy inference systems
provided with the Fuzzy Logic Toolbox. The FIS is always cast as a MATLAB
structure. To load this system (rather than bothering with creating it from
scratch), type
a = readfis('tipper.fis')
MATLAB will respond with
a =
name:
type:
andMethod:
orMethod:
defuzzMethod:
impMethod:
aggMethod:
input:
output:
rule:
'tipper'
'mamdani'
'min'
'max'
'centroid'
'min'
'max'
[1x2 struct]
[1x1 struct]
[1x3 struct]
The labels on the left of this listing represent the various components of the
MATLAB structure associated with tipper.fis. You can access the various
components of this structure by typing the component name after typing a. At
the MATLAB command line, type
a.type
for example. MATLAB will respond with
ans =
mamdani
The function
getfis(a)
returns almost the same structure information that typing a, alone does.
getfis(a) returns
Name
= tipper
Type
= mamdani
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NumInputs = 2
InLabels =
service
food
NumOutputs = 1
OutLabels =
tip
NumRules = 3
AndMethod = min
OrMethod = max
ImpMethod = min
AggMethod = max
DefuzzMethod = centroid
Notice that some of these fields are not part of the structure, a. Thus, you
cannot get information by typing a.Inlabels, but you can get it by typing:
getfis(a,'Inlabels')
Similarly, you can obtain structure information using getfis in this manner.
getfis(a,'input',1)
getfis(a,'output',1)
getfis(a,'input',1,'mf',1)
The structure.field syntax also generates this information. For more
information on the syntax for MATLAB structures, see Chapter 13, “Structures
and Cell Arrays,” in Using MATLAB.
For example, type
a.input
or
a.input(1).mf(1)
The function getfis is loosely modeled on the Handle Graphics® function get.
There is also a function called setfis that acts as the reciprocal to getfis. It
allows you to change any property of an FIS. For example, if you wanted to
change the name of this system, you could type
a = setfis(a,'name','gratuity');
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However, since a is already a MATLAB structure, you can set this information
more simply by typing
a.name = 'gratuity';
Now the FIS structure a has been changed to reflect the new name. If you want
a little more insight into this FIS structure, try
showfis(a)
This returns a printout listing all the information about a. This function is
intended more for debugging than anything else, but it shows all the
information recorded in the FIS structure
Since the variable, a, designates the fuzzy tipping system, you can call up any
of the GUIs for the tipping system directly from the command line. Any of the
following will bring up the tipping system with the associated GUI.
• fuzzy(a): brings up the FIS Editor
• mfedit(a): brings up the Membership Function Editor
• ruleedit(a): brings up the Rule Editor
• ruleview(a): brings up the Rule Viewer
• surfview(a): brings up the Surface Viewer
If, in addition, a is a Sugeno-type FIS, then anfisedit(a) will bring up the
ANFIS Editor GUI.
Once any of these GUIs has been opened, you can access any of the other GUIs
using the pull-down menu rather than the command line.
System Display Functions
There are three functions designed to give you a high-level view of your fuzzy
inference system from the command line: plotfis, plotmf, and gensurf. The
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first of these displays the whole system as a block diagram much as it would
appear on the FIS Editor.
plotfis(a)
After closing any open MATLAB figures or GUI windows, the function plotmf
plots all the membership functions associated with a given variable as follows:
plotmf(a,'input',1)
returns
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poor
1
good
excellent
Degree of membership
0.8
0.6
0.4
0.2
0
0
1
2
3
4
5
service
6
7
8
9
10
plotmf(a,'output',1)
cheap
average
generous
1
Degree of membership
0.8
0.6
0.4
0.2
0
0
5
10
15
tip
20
25
30
These plots will appear in the Membership Function Editor GUI, or in an open
MATLAB figure, if plotmf is called while either of these is open.
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Finally, the function gensurf will plot any one or two inputs versus any one
output of a given system. The result is either a two-dimensional curve, or a
three-dimensional surface. Note that when there are three or more inputs,
gensurf must be generated with all but two inputs fixed, as is described in the
description of genfis in Chapter 3, “Reference” .
gensurf(a)
25
tip
20
15
10
5
10
8
10
6
8
6
4
4
2
food
2
0
0
service
Building a System from Scratch
It is possible to use the Fuzzy Logic Toolbox without bothering with the GUI
tools at all. For instance, to build the tipping system entirely from the
command line, you would use the commands newfis, addvar, addmf, and
addrule.
Probably the trickiest part of this process is learning the shorthand that the
fuzzy inference systems use for building rules. This is accomplished using the
command line function, addrule.
Each variable, input, or output, has an index number, and each membership
function has an index number. The rules are built from statements like this
if input1 is MF1 or input2 is MF3 then output1 is MF2 (weight = 0.5)
This rule is turned into a structure according to the following logic: If there are
m inputs to a system and n outputs, then the first m vector entries of the rule
structure correspond to inputs 1 through m. The entry in column 1 is the index
number for the membership function associated with input 1. The entry in
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column 2 is the index number for the membership function associated with
input 2. And so on. The next n columns work the same way for the outputs.
Column m + n + 1 is the weight associated with that rule (typically 1) and
column m + n + 2 specifies the connective used (where AND = 1 and OR = 2).
The structure associated with the rule shown above is
1 3 2 0.5 2
Here is one way you can build the entire tipping system from the command
line, using the MATLAB structure syntax.
a=newfis('tipper');
a.input(1).name='service';
a.input(1).range=[0 10];
a.input(1).mf(1).name='poor';
a.input(1).mf(1).type='gaussmf';
a.input(1).mf(1).params=[1.5 0];
a.input(1).mf(2).name='good';
a.input(1).mf(2).type='gaussmf';
a.input(1).mf(2).params=[1.5 5];
a.input(1).mf(3).name='excellent';
a.input(1).mf(3).type='gaussmf';
a.input(1).mf(3).params=[1.5 10];
a.input(2).name='food';
a.input(2).range=[0 10];
a.input(2).mf(1).name='rancid';
a.input(2).mf(1).type='trapmf';
a.input(2).mf(1).params=[-2 0 1 3];
a.input(2).mf(2).name='delicious';
a.input(2).mf(2).type='trapmf';
a.input(2).mf(2).params=[7 9 10 12];
a.output(1).name='tip';
a.output(1).range=[0 30];
a.output(1).mf(1).name='cheap'
a.output(1).mf(1).type='trimf';
a.output(1).mf(1).params=[0 5 10];
a.output(1).mf(2).name='average';
a.output(1).mf(2).type='trimf';
a.output(1).mf(2).params=[10 15 20];
a.output(1).mf(3).name='generous';
a.output(1).mf(3).type='trimf';
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a.output(1).mf(3).params=[20 25 30];
a.rule(1).antecedent=[1 1];
a.rule(1).consequent=[1];
a.rule(1).weight=1;
a.rule(1).connection=2;
a.rule(2).antecedent=[2 0];
a.rule(2).consequent=[2];
a.rule(2).weight=1;
a.rule(2).connection=1;
a.rule(3).antecedent=[3 2];
a.rule(3).consequent=[3];
a.rule(3).weight=1;
a.rule(3).connection=2
Alternatively, here is how you can build the entire tipping system from the
command line using Fuzzy Logic Toolbox commands.
a=newfis('tipper');
a=addmf(a,'input',1,'service',[0 10]);
a=addmf(a,'input',1,'poor','gaussmf',[1.5 0]);
a=addmf(a,'input',1,'good','gaussmf',[1.5 5]);
a=addmf(a,'input',1,'excellent','gaussmf',[1.5 10]);
a=addvar(a,'input','food',[0 10]);
a=addmf(a,'input',2,'rancid','trapmf',[-2 0 1 3]);
a=addmf(a,'input',2,'delicious','trapmf',[7 9 10 12]);
a=addvar(a,'output','tip',[0 30]);
a=addmf(a,'output',1,'cheap','trimf',[0 5 10]);
a=addmf(a,'output',1,'average','trimf',[10 15 20]);
a=addmf(a,'output',1,'generous','trimf',[20 25 30]);
ruleList=[ ...
1 1 1 1 2
2 0 2 1 1
3 2 3 1 2 ];
a=addrule(a,ruleList);
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FIS Evaluation
To evaluate the output of a fuzzy system for a given input, use the function
evalfis. For example, the following script evaluates tipper at the input, [1 2].
a = readfis('tipper');
evalfis([1 2], a)
ans =
5.5586
This function can also be used for multiple collections of inputs, since different
input vectors are represented in different parts of the input structure. By doing
multiple evaluations at once, you get a tremendous boost in speed.
evalfis([3 5; 2 7], a)
ans =
12.2184
7.7885
The FIS Structure
The FIS structure is the MATLAB object that contains all the fuzzy inference
system information. This structure is stored inside each GUI tool. Access
functions such as getfis and setfis make it easy to examine this structure.
You can also access the FIS structure information using the structure.field
syntax (see the section, “Working from the Command Line” on page 2-65).
All the information for a given fuzzy inference system is contained in the FIS
structure, including variable names, membership function definitions, and so
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on. This structure can itself be thought of as a hierarchy of structures, as shown
in the diagram below:
input1 MFs
input
FIS
name
type
andMethod
orMethod
defuzzMethod
impMethod
aggMethod
input
output
rule
name
range
mf
name
type
params
output
name
range
mf
input2 MFs
name
type
params
rules
antecedent
consequent
weight
connections
output MFs
name
type
params
You can generate a listing of information on the FIS using the showfis
command, as shown below.
showfis(a)
1. Name
2. Type
3. Inputs/Outputs
4. NumInputMFs
5. NumOutputMFs
6. NumRules
7. AndMethod
8. OrMethod
9. ImpMethod
10. AggMethod
11. DefuzzMethod
12. InLabels
13.
14. OutLabels
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tipper
mamdani
[ 2 1 ]
[ 3 2 ]
3
3
min
max
min
max
centroid
service
food
tip
Working from the Command Line
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
42.
43.
44.
42.
43.
44.
42.
43.
44.
InRange
OutRange
InMFLabels
OutMFLabels
InMFTypes
OutMFTypes
InMFParams
OutMFParams
Rule Antecedent
Rule Consequent
Rule Weigth
Rule Connection
[ 0 10 ]
[ 0 10 ]
[ 0 30 ]
poor
good
excellent
rancid
delicious
cheap
average
generous
gaussmf
gaussmf
gaussmf
trapmf
trapmf
trimf
trimf
trimf
[ 1.5 0 0 0 ]
[ 1.5 5 0 0 ]
[ 1.5 10 0 0 ]
[ 0 0 1 3 ]
[ 7 9 10 10 ]
[ 0 5 10 0 ]
[ 10 15 20 0 ]
[ 20 25 30 0 ]
[ 1 1 ]
[ 2 0 ]
[ 3 2 ]
1
2
3
1
1
1
2
1
2
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The list of command line functions associated with FIS construction include
getfis, setfis, showfis, addvar, addmf, addrule, rmvar, and rmmf.
Saving FIS Files on Disk
A specialized text file format is used for saving fuzzy inference systems to a
disk. The functions readfis and writefis are used for reading and writing
these files.
If you prefer, you can modify the FIS by editing its .fis text file rather than
using any of the GUIs. You should be aware, however, that changing one entry
may oblige you to change another. For example, if you delete a membership
function using this method, you also need to make certain that any rules
requiring this membership function are also deleted.
The rules appear in “indexed” format in a .fis text file. Here is the file
tipper.fis.
[System]
Name='tipper'
Type='mamdani'
NumInputs=2
NumOutputs=1
NumRules=3
AndMethod='min'
OrMethod='max'
ImpMethod='min'
AggMethod='max'
DefuzzMethod='centroid'
[Input1]
Name='service'
Range=[0 10]
NumMFs=3
MF1='poor':'gaussmf',[1.5 0]
MF2='good':'gaussmf',[1.5 5]
MF3='excellent':'gaussmf',[1.5 10]
[Input2]
Name='food'
Range=[0 10]
NumMFs=2
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Working from the Command Line
MF1='rancid':'trapmf',[0 0 1 3]
MF2='delicious':'trapmf',[7 9 10 10]
[Output1]
Name='tip'
Range=[0 30]
NumMFs=3
MF1='cheap':'trimf',[0 5 10]
MF2='average':'trimf',[10 15 20]
MF3='generous':'trimf',[20 25 30]
[Rules]
1 1, 1 (1) : 2
2 0, 2 (1) : 1
3 2, 3 (1) : 2
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Working with Simulink
The Fuzzy Logic Toolbox is designed to work seamlessly with Simulink, the
simulation software available from The MathWorks. Once you’ve created your
fuzzy system using the GUI tools or some other method, you’re ready to embed
your system directly into a simulation.
An Example: Water Level Control
Picture a tank with a pipe flowing in and a pipe flowing out. You can change
the valve controlling the water that flows in, but the outflow rate depends on
the diameter of the outflow pipe (which is constant) and the pressure in the
tank (which varies with the water level). The system has some very nonlinear
characteristics.
A controller for the water level in the tank needs to know the current water
level and it needs to be able to set the valve. Our controller’s input will be the
water level error (desired water level minus actual water level) and its output
will be the rate at which the valve is opening or closing. A first pass at writing
a fuzzy controller for this system might be the following.
1. If (level is okay) then (valve is no_change) (1)
2. If (level is low) then (valve is open_fast) (1)
3. If (level is high) then (valve is close_fast) (1)
One of the great advantages of the Fuzzy Logic Toolbox is the ability to take
fuzzy systems directly into Simulink and test them out in a simulation
environment. A Simulink block diagram for this system is shown below. It
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Working with Simulink
contains a Simulink block called the Fuzzy Logic Controller block. The
Simulink block diagram for this system is sltank. Typing
sltank
at the command line, causes the system to appear.
At the same time, the file tank.fis is loaded into the FIS structure tank.
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Some experimentation shows that three rules are not sufficient, since the
water level tends to oscillate around the desired level. This is seen from the plot
below.
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
10
20
30
40
50
60
Time (second)
70
80
90
100
We need to add another input, the water level’s rate of change, to slow down
the valve movement when we get close to the right level.
4. If (level is good) and (rate is negative) then (valve is close_slow) (1)
5. If (level is good) and (rate is positive) then (valve is open_slow) (1)
The demo, sltank is built with these five rules. You can examine With all five
rules in operations, the step response by simulating this system. This is done
by clicking on Start from the pull-down menu under Simulate, and clicking on
the Comparison block. The result looks like this
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
10
20
30
40
50
60
Time (second)
70
80
90
100
One interesting feature of the water tank system is that the tank empties much
more slowly than it fills up because of the specific value of the outflow diameter
pipe. We can deal with this by setting the close_slow valve membership
function to be slightly different from the open_slow setting. A PID controller
does not have this capability. The valve command versus the water level
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Working with Simulink
change rate (depicted as water) and the relative water level change (depicted
as level) surface looks like this. If you look closely, you can see a slight
asymmetry to the plot.
0.8
0.6
0.4
valve
0.2
0
−0.2
−0.4
−0.6
1
−0.8
0.5
0.1
0
0.05
0
−0.5
−0.05
water
−0.1
−1
level
Because the MATLAB technical computing environment supports so many
tools (like the Control System Toolbox, the Neural Network Toolbox, the
Nonlinear Control Design Blockset, and so on), you can, for example, easily
make a comparison of a fuzzy controller versus a linear controller or a neural
network controller.
For a demonstration of how the Rule Viewer can be used to interact with a
Fuzzy Logic Controller block in a Simulink model, type
sltankrule
This demo contains a block called the Fuzzy Controller With Rule Viewer block.
In this demo, the Rule Viewer opens when you start the Simulink simulation.
This Rule Viewer provides an animation of how the rules are fired during the
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water tank simulation. The windows that open when you simulate the
sltankrule demo are depicted as follows:
The Rule Viewer that opens during the simulation can be used to access the
Membership Function Editor, the Rule Editor, or any of the other GUIs, (see
“The Membership Function Editor” on page 2-52, or “The Rule Editor” on page
2-56, for more information).
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For example, you may want to open the Rule Editor to change one of your rules.
To do so, select the Edit rules menu item under the View menu of the open
Rule Viewer. Now you can view or edit the rules for this Simulink model:
It’s best if you stop the simulation prior to selecting any of these editors to
change your FIS. Remember to save any changes you make to your FIS to the
workspace before you restart the simulation.
Building Your Own Fuzzy Simulink Models
To build your own Simulink systems that use fuzzy logic, simply copy the Fuzzy
Logic Controller block out of sltank (or any of the other Simulink demo
systems available with the toolbox) and place it in your own block diagram. You
can also open the Simulink library called fuzblock, which contains the Fuzzy
Logic Controller block, the Fuzzy Controller With Rule Viewer block, and
several demo blocks. To access these blocks, type
fuzblock
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at the MATLAB prompt. The following library appears:
When you use these blocks, make sure that the fuzzy inference system (FIS)
structure corresponding to your fuzzy system is both in the MATLAB
workspace, and referred to by name in the dialog box associated with the Fuzzy
Logic Controller block.
Double-click on the Fuzzy Controller With Rule Viewer block, and the following
appears:
This block uses the zero-order hold method for sampling.
The Fuzzy Logic Controller block is a masked Simulink block based on the
S-function sffis.mex. This function is itself based on the same algorithms as
the function evalfis, but it has been tailored to work optimally within the
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Simulink environment. For more descriptions of these, see fuzblock on page
3-27, and sffis on page 3-65.
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Sugeno-Type Fuzzy Inference
The fuzzy inference process we’ve been referring to so far is known as
Mamdani’s fuzzy inference method. It’s the most commonly seen fuzzy
methodology. In this section we discuss the so-called Sugeno, or
Takagi-Sugeno-Kang method of fuzzy inference first introduced in 1985
[Sug85]. It is similar to the Mamdani method in many respects. In fact the first
two parts of the fuzzy inference process, fuzzifying the inputs and applying the
fuzzy operator, are exactly the same. The main difference between
Mamdani-type of fuzzy inference and Sugeno-type is that the output
membership functions are only linear or constant for Sugeno-type fuzzy
inference.
A typical fuzzy rule in a zero-order Sugeno fuzzy model has the form
if x is A and y is B then z = k
where A and B are fuzzy sets in the antecedent, while k is a crisply defined
constant in the consequent. When the output of each rule is a constant like this,
the similarity with Mamdani’s method is striking. The only distinctions are the
fact that all output membership functions are singleton spikes, and the
implication and aggregation methods are fixed and can not be edited. The
implication method is simply multiplication, and the aggregation operator just
includes all of the singletons.
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1.
If
poor
cheap
rancid
service is poor
or
3. Apply
implication
method (min).
2. Apply
fuzzy
operation
(OR = max)
1. Fuzzify inputs
food is rancid
then
tip = cheap
average
2.
rule 2 has
no dependency
on input 2
good
If
3.
If
service is good
then
tip = average
excellent
generous
delicious
service is excellent
or
food is delicious
service = 3
food = 8
input 1
input 2
then
tip = generous
output
tip = 16.3%
4. Apply
aggregation
method (max).
5. Defuzzify
(weighted
average)
The figure above shows the fuzzy tipping model developed in previous sections
of this manual adapted for use as a zero-order Sugeno system. Fortunately it is
frequently the case that singleton output functions are completely sufficient for
a given problem’s needs. As an example, the system tippersg.fis is the
Sugeno-type representation of the now-familiar tipping model. If you load the
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system and plot its output surface, you will see it is almost the same as the
Mamdani system we’ve been looking at.
a = readfis('tippersg');
gensurf(a)
tip
20
15
10
10
8
10
6
8
6
4
4
2
food
2
0
0
service
The more general first-order Sugeno fuzzy model has rules of the form
if x is A and y is B then z = p*x + q*y + r
where A and B are fuzzy sets in the antecedent, while p, q, and r are all
constants. The easiest way to visualize the first-order system is to think of each
rule as defining the location of a “moving singleton.” That is, the singleton
output spikes can move around in a linear fashion in the output space,
depending on what the input is. This also tends to make the system notation
very compact and efficient. Higher order Sugeno fuzzy models are possible, but
they introduce significant complexity with little obvious merit. Sugeno fuzzy
models whose output membership functions are greater than first order are not
supported by the Fuzzy Logic Toolbox.
Because of the linear dependence of each rule on the system’s input variables,
the Sugeno method is ideal for acting as an interpolating supervisor of multiple
linear controllers that are to be applied, respectively, to different operating
conditions of a dynamic nonlinear system. For example, the performance of an
aircraft may change dramatically with altitude and Mach number. Linear
controllers, though easy to compute and well-suited to any given flight
condition, must be updated regularly and smoothly to keep up with the
changing state of the flight vehicle. A Sugeno fuzzy inference system is
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extremely well suited to the task of smoothly interpolating the linear gains
that would be applied across the input space; it’s a natural and efficient gain
scheduler. Similarly, a Sugeno system is suited for modeling nonlinear systems
by interpolating multiple linear models.
An Example: Two Lines
To see a specific example of a system with linear output membership functions,
consider the one input one output system stored in sugeno1.fis.
fismat = readfis('sugeno1');
getfis(fismat,'output',1)
Name = output
NumMFs = 2
MFLabels =
line1
line2
Range = [0 1]
The output variable has two membership functions:
getfis(fismat,'output',1,'mf',1)
Name = line1
Type = linear
Params =
–1
–1
getfis(fismat,'output',1,'mf',2)
Name = line2
Type = linear
Params =
1
–1
Further, these membership functions are linear functions of the input variable.
The membership function line1 is defined by the equation
output = (-1)*input + (-1)
and the membership function line2 is defined by the equation
output = (1)*input + (-1)
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The input membership functions and rules define which of these output
functions will be expressed and when.
showrule(fismat)
ans =
1. If (input is low) then (output is line1) (1)
2. If (input is high) then (output is line2) (1)
The function plotmf shows us that the membership function low generally
refers to input values less than zero, while high refers to values greater than
zero. The function gensurf shows how the overall fuzzy system output switches
smoothly from the line called line1 to the line called line2.
subplot(2,1,1), plotmf(fismat,'input',1)
subplot(2,1,2), gensurf(fismat)
low
high
Degree of belief
1
0.8
0.6
0.4
0.2
0
−5
−4
−3
−2
−1
0
input
1
2
3
4
5
−4
−3
−2
−1
0
input
1
2
3
4
5
4
output
3
2
1
0
−1
−5
This is just one example of how a Sugeno-type system gives you the freedom to
incorporate linear systems into your fuzzy systems. By extension, you could
build a fuzzy system that switches between several optimal linear controllers
as a highly nonlinear system moves around in its operating space.
Conclusion
Because it is a more compact and computationally efficient representation than
a Mamdani system, the Sugeno system lends itself to the use of adaptive
techniques for constructing fuzzy models. These adaptive techniques can be
used to customize the membership functions so that the fuzzy system best
models the data.
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Sugeno-Type Fuzzy Inference
Note on FIS Conversion: The MATLAB command line function mam2sug can
be used to convert a Mamdani system into a Sugeno system (not necessarily
with a single output) with constant output membership functions. It uses the
centroid associated with all of the output membership functions of the
Mamdani system. See Chapter 3 for details.
Here are some final considerations about the two different methods:
Advantages of the Sugeno method
• It’s computationally efficient.
• It works well with linear techniques (e.g., PID control).
• It works well with optimization and adaptive techniques.
• It has guaranteed continuity of the output surface.
• It’s well-suited to mathematical analysis.
Advantages of the Mamdani method
• It’s intuitive.
• It has widespread acceptance.
• It’s well-suited to human input.
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anfis and the ANFIS Editor GUI
The basic structure of the type of fuzzy inference system that we’ve seen thus
far is a model that maps input characteristics to input membership functions,
input membership function to rules, rules to a set of output characteristics,
output characteristics to output membership functions, and the output
membership function to a single-valued output or a decision associated with
the output. We have only considered membership functions that have been
fixed, and somewhat arbitrarily chosen. Also, we’ve only applied fuzzy
inference to modeling systems whose rule structure is essentially
predetermined by the user’s interpretation of the characteristics of the
variables in the model.
In this section we discuss the use of the function anfis and the ANFIS Editor
GUI in the Fuzzy Logic Toolbox. These tools apply fuzzy inference techniques
to data modeling. As you have seen from the other fuzzy inference GUIs, the
shape of the membership functions depends on parameters, and changing
these parameters will change the shape of the membership function. Instead of
just looking at the data to choose the membership function parameters, we will
see how membership function parameters can be chosen automatically using
these Fuzzy Logic Toolbox applications.
A Modeling Scenario
Suppose you want to apply fuzzy inference to a system for which you already
have a collection of input/output data that you would like to use for modeling,
model-following, or some similar scenario. You don’t necessarily have a
predetermined model structure based on characteristics of variables in your
system.
There will be some modeling situations in which you can’t just look at the data
and discern what the membership functions should look like. Rather than
choosing the parameters associated with a given membership function
arbitrarily, these parameters could be chosen so as to tailor the membership
functions to the input/output data in order to account for these types of
variations in the data values. This is where the so-called neuro-adaptive
learning techniques incorporated into anfis in the Fuzzy Logic Toolbox can
help.
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Model Learning and Inference Through ANFIS
The basic idea behind these neuro-adaptive learning techniques is very simple.
These techniques provide a method for the fuzzy modeling procedure to learn
information about a data set, in order to compute the membership function
parameters that best allow the associated fuzzy inference system to track the
given input/output data. This learning method works similarly to that of
neural networks. The Fuzzy Logic Toolbox function that accomplishes this
membership function parameter adjustment is called anfis. anfis can be
accessed either from the command line, or through the ANFIS Editor GUI.
Since the functionality of the command line function anfis and the ANFIS
Editor GUI is similar, they are used somewhat interchangeably in this
discussion, until we distinguish them through the description of the GUI.
What Is ANFIS?
The acronym ANFIS derives its name from adaptive neuro-fuzzy inference
system. Using a given input/output data set, the toolbox function anfis
constructs a fuzzy inference system (FIS) whose membership function
parameters are tuned (adjusted) using either a backpropagation algorithm
alone, or in combination with a least squares type of method. This allows your
fuzzy systems to learn from the data they are modeling.
FIS Structure and Parameter Adjustment
A network-type structure similar to that of a neural network, which maps
inputs through input membership functions and associated parameters, and
then through output membership functions and associated parameters to
outputs, can be used to interpret the input/output map.
The parameters associated with the membership functions will change
through the learning process. The computation of these parameters (or their
adjustment) is facilitated by a gradient vector, which provides a measure of
how well the fuzzy inference system is modeling the input/output data for a
given set of parameters. Once the gradient vector is obtained, any of several
optimization routines could be applied in order to adjust the parameters so as
to reduce some error measure (usually defined by the sum of the squared
difference between actual and desired outputs). anfis uses either back
propagation or a combination of least squares estimation and backpropagation
for membership function parameter estimation.
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Familiarity Breeds Validation: Know Your Data
The modeling approach used by anfis is similar to many system identification
techniques. First, you hypothesize a parameterized model structure (relating
inputs to membership functions to rules to outputs to membership functions,
and so on). Next, you collect input/output data in a form that will be usable by
anfis for training. You can then use anfis to train the FIS model to emulate
the training data presented to it by modifying the membership function
parameters according to a chosen error criterion.
In general, this type of modeling works well if the training data presented to
anfis for training (estimating) membership function parameters is fully
representative of the features of the data that the trained FIS is intended to
model. This is not always the case, however. In some cases, data is collected
using noisy measurements, and the training data cannot be representative of
all the features of the data that will be presented to the model. This is where
model validation comes into play.
Model Validation Using Checking and Testing Data Sets
Model validation is the process by which the input vectors from input/output
data sets on which the FIS was not trained, are presented to the trained FIS
model, to see how well the FIS model predicts the corresponding data set
output values. This is accomplished with the ANFIS Editor GUI using the
so-called testing data set, and its use is described in a subsection that follows.
You can also use another type of data set for model validation in anfis. This
other type of validation data set is referred to as the checking data set and this
set is used to control the potential for the model overfitting the data. When
checking data is presented to anfis as well as training data, the FIS model is
selected to have parameters associated with the minimum checking data model
error.
One problem with model validation for models constructed using adaptive
techniques is selecting a data set that is both representative of the data the
trained model is intended to emulate, yet sufficiently distinct from the training
data set so as not to render the validation process trivial. If you have collected
a large amount of data, hopefully this data contains all the necessary
representative features, so the process of selecting a data set for checking or
testing purposes is made easier. However, if you expect to be presenting noisy
measurements to your model, it’s possible the training data set does not
include all of the representative features you want to model.
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The basic idea behind using a checking data set for model validation is that
after a certain point in the training, the model begins overfitting the training
data set. In principle, the model error for the checking data set tends to
decrease as the training takes place up to the point that overfitting begins, and
then the model error for the checking data suddenly increases. In the first
example in the following section, two similar data sets are used for checking
and training, but the checking data set is corrupted by a small amount of noise.
This example illustrates of the use of the ANFIS Editor GUI with checking
data to reduce the effect of model overfitting. In the second example, a training
data set that is presented to anfis is sufficiently different than the applied
checking data set. By examining the checking error sequence over the training
period, it is clear that the checking data set is not good for model validation
purposes. This example illustrates the use of the ANFIS Editor GUI to compare
data sets.
Some Constraints of anfis
anfis is much more complex than the fuzzy inference systems discussed so far,
and is not available for all of the fuzzy inference system options. Specifically,
anfis only supports Sugeno-type systems, and these must be:
• First or zeroth order Sugeno-type systems
• Single output, obtained using weighted average defuzzification (linear or
constant output membership functions)
• Of unity weight for each rule
An error occurs if your FIS structure does not comply with these constraints.
Moreover, anfis cannot accept all the customization options that basic fuzzy
inference allows. That is, you cannot make your own membership functions
and defuzzification functions; you’ll have to use the ones provided.
The ANFIS Editor GUI
To get started with the ANFIS Editor GUI, type
anfisedit
The following GUI will appear on your screen.
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Load or save a fuzzy
Sugeno system, or open
new Sugeno system.
Undo
Open or edit a FIS with any
of the other GUIs.
Plot region
Status of the number of inputs, outputs,
input membership functions, and output
membership functions
Testing data appears on the
plot in blue as ..
Training data appears on
the plot in blue as o o
After you generate
or load a FIS, this
button allows you to
open a graphical
representation of its
input/output
structure.
Checking data appears on
the plot in blue as ++
FIS output appears on the
plot in red as **
Load either training,
testing, or checking
data from disk or
workspace, or load
demo data. Data
appears in the plot
region.
Test data against
the FIS model. The
plot appears in the
plot region.
Clear Data unloads the data set
selected under Type:
and clears the plot region.
Load FIS or generate FIS
from loaded data using
your chosen number of MFs
and rules or fuzzy.
Train FIS after setting optimization
method, error tolerance, and number
of epochs. This generates error plots
in the plot region.
From this GUI you can
• Load data (training, testing, and checking) by selecting appropriate radio
buttons in the Load data portion of the GUI and then selecting Load Data...
The loaded data is plotted on the plot region.
• Generate an initial FIS model or load an initial FIS model using the options
in the Generate FIS portion of the GUI
• View the FIS model structure once an initial FIS has been generated or
loaded by selecting the Structure button
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• Choose the FIS model parameter optimization method: backpropagation or a
mixture of backpropagation and least squares (hybrid method)
• Choose the number of training epochs and the training error tolerance
• Train the FIS model by selecting the Train Now button
This training adjusts the membership function parameters and plots the
training (and/or checking data) error plot(s) in the plot region.
• View the FIS model output versus the training, checking, or testing data
output by selecting the Test Now button
This function plots the test data against the FIS output in the plot region.
You can also use the ANFIS Editor GUI menu bar to load an FIS training
initialization, save your trained FIS, open a new Sugeno system, or open any
of the other GUIs to interpret the trained FIS model.
Data Formalities and the ANFIS Editor GUI: Checking and Training
To start training an FIS using either anfis or the ANFIS Editor GUI, first you
need to have a training data set that contains desired input/output data pairs
of the target system to be modeled. Sometimes you also want to have the
optional testing data set that can check the generalization capability of the
resulting fuzzy inference system, and/or a checking data set that helps with
model overfitting during the training. The use of a testing data set and a
checking data set for model validation is discussed in “Model Validation Using
Checking and Testing Data Sets” on page 2-94. As we mentioned previously,
overfitting is accounted for by testing the FIS trained on the training data
against the checking data, and choosing the membership function parameters
to be those associated with the minimum checking error if these errors indicate
model overfitting. You will have to examine your training error plots fairly
closely in order to determine this. These issues are discussed later in an
example. Usually these training and checking data sets are collected based on
observations of the target system and are then stored in separate files.
Note on Data Format: Any data set you load into the ANFIS Editor GUI, (or
that is applied to the command line function anfis) must be a matrix with the
input data arranged as vectors in all but the last column. The output data
must be in the last column.
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ANFIS Editor GUI Example 1: Checking Data Helps
Model Validation
In this section we look at an example that loads similar training and checking
data sets, only the checking data set is corrupted by noise.
Loading Data
To work both of the following examples, you load the training data sets
(fuzex1trnData and fuzex2trnData) and the checking data sets
(fuzex1chkData and fuzex2chkData), into the ANFIS Editor GUI from the
workspace. You may also substitute your own data sets.
To load these data sets from the directory fuzzydemos into the MATLAB
workspace, type
load
load
load
load
fuzex1trnData.dat
fuzex2trnData.dat
fuzex1chkData.dat
fuzex2chkData.dat
from the command line.
Note on loading data: You may also want to load your data set from the
fuzzydemos or any other directory on the disk, using the ANFIS Editor GUI,
directly.
Open the ANFIS Editor GUI by typing anfisedit. To load the training data
set, click on Training, worksp. and then Load Data....
The small GUI window that pops up allows you to type in a variable name from
the workspace. Type in fuzex1trnData, as shown below.
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The training data appears in the plot in the center of the GUI as a set of circles.
Notice the horizontal axis is marked data set index. This index indicates the
row from which that input data value was obtained (whether or not the input
is a vector or a scalar). Next click on Checking in the Type column of the Load
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data portion of the GUI to load fuzex1chkData from the workspace. This data
appears in the GUI plot as plusses superimposed on the training data.
+++ Checking data
ooo Training data
This data set will be used to train a fuzzy system by adjusting the membership
function parameters that best model this data. The next step is to specify an
initial fuzzy inference system for anfis to train.
Initializing and Generating Your FIS
You can either initialize the FIS parameters to your own preference, or if you
do not have any preference for how you want the initial membership functions
to be parameterized, you can let anfis do this for you.
Automatic FIS Structure Generation with ANFIS
To initialize your FIS using anfis:
1 Choose Grid partition, the default partitioning method. (The two partition
methods, grid partitioning and subtractive clustering, are described later in
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“Fuzzy C-Means Clustering” on page 2-120, and in “Subtractive Clustering”
on page 2-123.
2 Click on the Generate FIS button. This brings up a menu from which you
can choose the number of membership functions, MFs, and the type of input
and output membership functions. Notice there are only two choices for the
output membership function: constant and linear. This limitation of output
membership function choices is because anfis only operates on Sugeno-type
systems.
3 Fill in the entries as we’ve done below, and click on OK.
You can also implement this FIS generation from the command line using the
command genfis1 (for grid partitioning) or genfis2 (for subtractive
clustering). A command line language example illustrating the use of genfis1
and anfis is provided later.
Specifying Your Own Membership Functions for ANFIS
Although we don’t expect you to do this for this example, you can choose your
own preferred membership functions with specific parameters to be used by
anfis as an initial FIS for training.
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To define your own FIS structure and parameters:
1 Open the Edit membership functions menu item from the View menu.
2 Add your desired membership functions (the custom membership option will
be disabled for anfis). The output membership functions must either be all
constant or all linear. For carrying out this and the following step, see “The
FIS Editor” on page 2-49 and “The Membership Function Editor” on page
2-52.
3 Select the Edit rules menu item in the View menu. Use the Rule Editor to
generate the rules (see“The Rule Editor” on page 2-56).
4 Select the Edit FIS properties menu item from the View menu. Name your
FIS, and save it to either the workspace or the disk.
5 Use the View menu to return to the ANFIS Editor GUI to train the FIS.
To load an existing FIS for ANFIS initialization, in the Generate FIS portion
of the GUI, click on Load from worksp. or Load from disk. You will load your
FIS from the disk if you have saved an FIS previously that you would like to
use. Otherwise you will be loading your FIS from the workspace. Either of
these radio buttons toggle the Generate FIS button to Load.... Load your FIS
by clicking on this button.
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Viewing Your FIS Structure
After you generate the FIS, you can view the model structure by clicking on the
Structure button in the middle of the right-hand side of the GUI. A new GUI
appears, as follows:
Return to other open GUIs
using the Window menu.
Color coding of branches
characterizes the rules.
Node labels - for
example, leftmost
node is the
input node
Node representing a normalization
factor for the rules.
The branches in this nodal graph are color coded to indicate whether or not
and, not, or or, are used in the rules. Clicking on the nodes indicates
information about the structure.
You can view the membership functions or the rules by opening either the
Membership Function Editor, or the Rule Editor from the View menu.
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ANFIS Training
The two anfis parameter optimization method options available for FIS
training are hybrid (the default, mixed least squares and backpropagation)
and backpropa (backpropagation). The Error Tolerance is used to create a
training stopping criterion, which is related to the error size. The training will
stop after the training data error remains within this tolerance. This is best left
set to 0 if you don’t know how your training error is going to behave.
To start the training:
• Leave the optimization method at hybrid.
• Set the number of training epochs to 40, under the Epochs listing on the GUI
(the default value is 3).
• Select Train Now.
The following should appear on your screen:
*** Training error
... Checking error
Notice how the checking error decreases up to a certain point in the training
and then it increases. This increase represents the point of model overfitting.
anfis chooses the model parameters associated with the minimum checking
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error (just prior to this jump point). This is an example for which the checking
data option of anfis is useful.
Testing Your Data Against the Trained FIS
To test your FIS against the checking data, click on Checking data in the Test
FIS portion of the GUI, and click on Test Now. Now when you test the checking
data against the FIS it looks pretty good:
Note on loading more data with anfis: If you are ever loading data into
anfis after clearing previously loaded data, you must make sure that the
newly loaded data sets have the same number of inputs as the previously
loaded ones did. Otherwise you will have to start a new anfisedit session
from the command line.
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Note on the Checking Data option and Clearing Data: If you don’t want to
use the checking data option of anfis, don’t load any checking data before you
train the FIS. If you decide to retrain your FIS with no checking data, you can
unload the checking data in one of two ways. One method is to click on the
Checking radio button in the Load data portion of the GUI and then click on
Clear Data to unload the checking data. The other method you can use is to
close the GUI and go to the command line and retype anfisedit. In this case
you will have to reload the training data. After clearing the data, you will need
to regenerate your FIS. Once the FIS is generated you can use your first
training experience to decide on the number of training epochs you want for
the second round of training.
ANFIS Editor GUI Example 2: Checking Data Doesn’t
Validate Model
In this example, we examine what happens when the training and checking
data sets are sufficiently different. We see how the ANFIS Editor GUI can be
used to learn something about data sets and how they differ.
1 Clear both the training and checking data.
2 You can press the Clear Plot button on the right, although you don’t have to.
3 Load fuzex2trnData and fuzex2chkData (respectively, the training data
and checking data) from the MATLAB workspace just as you did in the
previous example.
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You should get something that looks like this:
+++ Checking data
ooo Training data
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Train the FIS for this system exactly as you did in the previous example, except
now choose 60 Epochs before training. You should get the following:
*** Training error
... Checking error
Notice the checking error is quite large. It appears that the minimum checking
error occurs within the first epoch. Recall that using the checking data option
with anfis automatically sets the FIS parameters to be those associated with
the minimum checking error. Clearly this set of membership functions would
not be the best choice for modeling the training data.
What’s wrong here? This example illustrates the problem discussed earlier
wherein the checking data set presented to anfis for training was sufficiently
different from the training data set. As a result, the trained FIS did not capture
the features of this data set very well. This illustrates the importance of
knowing the features of your data set well enough when you select your
training and checking data. When this is not the case, you can analyze the
checking error plots to see whether or not the checking data performed
sufficiently well with the trained model. In this example, the checking error is
sufficiently large to indicate that either more data needs to be selected for
training, or you may want to modify your membership function choices (both
the number of membership functions and the type). Otherwise the system can
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be retrained without the checking data, if you think the training data captures
sufficiently the features you are trying to represent.
To complete this example, let’s test the trained FIS model against the checking
data. To do so, click on Checking data in the Test FIS portion of the GUI, and
click on Test Now. The following plot in the GUI indicates that there is quite a
discrepancy between the checking data output and the FIS output.
anfis from the Command Line
As you can see, generating an FIS using the ANFIS Editor GUI is quite simple.
However, as you saw in the last example, you need to be cautious about
implementing the checking data validation feature of anfis. You must check
that the checking data error does what is supposed to. Otherwise you need to
retrain the FIS.
In this section we describe how to carry out the command line features of anfis
on a chaotic times-series prediction example.
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Using anfis for Chaotic Time Series Prediction
The demo mgtsdemo uses anfis to predict a time series that is generated by the
following Mackey-Glass (MG) time-delay differential equation:
0.2x ( t – τ )
·
x ( t ) = --------------------------------- – 0.1x ( t )
10
1 + x (t – τ)
This time series is chaotic, and so there is no clearly defined period. The series
will not converge or diverge, and the trajectory is highly sensitive to initial
conditions. This is a benchmark problem in the neural network and fuzzy
modeling research communities.
To obtain the time series value at integer points, we applied the fourth-order
Runge-Kutta method to find the numerical solution to the above MG equation;
the result was saved in the file mgdata.dat. Here we assume x(0) = 1.2, τ = 17,
and x(t) = 0 for t < 0. To plot the MG time series, type
load mgdata.dat
t = mgdata(:, 1); x = mgdata(:, 2); plot(t, x);
In time-series prediction we want to use known values of the time series up to
the point in time, say, t, to predict the value at some point in the future, say,
t+P. The standard method for this type of prediction is to create a mapping
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from D sample data points, sampled every ∆ units in time, (x(t-(D-1)∆),..., x(t-∆),
x(t)), to a predicted future value x(t+P). Following the conventional settings for
predicting the MG time series, we set D = 4 and ∆ = P = 6. For each t, the input
training data for anfis is a four dimensional vector of the following form:
w(t) = [x(t–18) x(t–12) x(t–6) x(t)]
The output training data corresponds to the trajectory prediction:
s(t) = x(t+6)
For each t, ranging in values from 118 to 1117, the training input/output data
will be a structure whose first component is the four-dimensional input w, and
whose second component is the output s. There will be 1000 input/output data
values. We use the first 500 data values for the anfis training (these become
the training data set), while the others are used as checking data for validating
the identified fuzzy model. This results in two 500-point data structures:
trnData and chkData.
Here is the code that generates this data:
for t=118:1117,
Data(t-117,:)=[x(t-18) x(t-12) x(t-6) x(t) x(t+6)];
end
trnData=Data(1:500, :);
chkData=Data(501:end, :);
To start the training, we need an FIS structure that specifies the structure and
initial parameters of the FIS for learning. This is the task of genfis1:
fismat = genfis1(trnData);
Since we did not specify numbers and types of membership functions used in
the FIS, default values are assumed. These defaults provide two generalized
bell membership functions on each of the four inputs, eight altogether. The
generated FIS structure contains 16 fuzzy rules with 104 parameters. In order
to achieve good generalization capability, it is important to have the number of
training data points be several times larger than the number parameters being
estimated. In this case, the ratio between data and parameters is about five
(500/104).
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The function genfis1 generates initial membership functions that are equally
spaced and cover the whole input space. You can plot the input membership
functions using the following commands.
subplot(2,2,1)
plotmf(fismat,
subplot(2,2,2)
plotmf(fismat,
subplot(2,2,3)
plotmf(fismat,
subplot(2,2,4)
plotmf(fismat,
'input', 1)
'input', 2)
'input', 3)
'input', 4)
These initial membership functions are shown below.
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0.6
0.8
1
Input 1
1.2
0
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0.6
0.8
1
Input 3
1.2
0
0.6
0.8
1
Input 2
1.2
0.6
0.8
1
Input 4
1.2
To start the training, type
[fismat1,error1,ss,fismat2,error2] = ...
anfis(trnData,fismat,[],[],chkData);
This takes about four minutes on a Sun SPARCstation 2 for 10 epochs of
training. Because the checking data option of anfis was invoked, the final FIS
you choose would ordinarily be the one associated with the minimum checking
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error. This is stored in fismat2. The following code will plot these new
membership functions:
subplot(2,2,1)
plotmf(fismat2,
subplot(2,2,2)
plotmf(fismat2,
subplot(2,2,3)
plotmf(fismat2,
subplot(2,2,4)
plotmf(fismat2,
'input', 1)
'input', 2)
'input', 3)
'input', 4)
Here is the result:.
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0
0.2
0.6
0.8
1
Input 1
1.2
0
1
1
0.8
0.8
0.6
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To plot the error signals type
plot([error1; error2]);
Here error1 and error2 are the root mean squared error for the training and
checking data, respectively.
In addition to these error plots, you may want to plot the FIS output versus the
training or checking data. To compare the original MG time series and the
fuzzy prediction side by side, try
anfis_output = evalfis([trnData; chkData], fismat2);
index = 125:1124;
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subplot(211), plot(t(index), [x(index) anfis_output]);
subplot(212), plot(t(index), x(index) – anfis_output);
MG Time Serise and ANFIS Prediction
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Note that the difference between the original MG time series and the anfis
estimated values is very small. This is why you can only see one curve in the
first plot. The prediction error is shown in the second plot with a much finer
scale. Note that we have only trained for 10 epochs. Better performance is
expected if we apply more extensive training.
More on anfis and the ANFIS Editor GUI
The command anfis takes at least two and at most six input arguments. The
general format is
[fismat1,trnError,ss,fismat2,chkError] = ...
anfis(trnData,fismat,trnOpt,dispOpt,chkData,method);
where trnOpt (training options), dispOpt (display options), chkData (checking
data), and method (training method), are optional. All of the output arguments
are also optional. In this section we discuss the arguments and range
components of the command line function anfis, as well as the analogous
functionality of the ANFIS Editor GUI.
When the ANFIS Editor GUI is invoked using anfisedit, only the training
data set must exist prior to implementing anfis.In addition, the step-size will
be fixed when the adaptive neuro-fuzzy system is trained using this GUI tool.
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Training Data
The training data, trnData, is a required argument to anfis, as well as to the
ANFIS Editor GUI. Each row of trnData is a desired input/output pair of the
target system to be modeled. Each row starts with an input vector and is
followed by an output value. Therefore, the number of rows of trnData is equal
to the number of training data pairs, and, since there is only one output, the
number of columns of trnData is equal to the number of inputs plus one.
Input FIS Structure
The input FIS structure, fismat, can be obtained either from any of the fuzzy
editors: the FIS Editor, the Membership Function Editor, and the Rule Editor
from the ANFIS Editor GUI, (which allows an FIS structure to be loaded from
the disk or the workspace), or from the command line function, genfis1 (for
which you only need to give numbers and types of membership functions). The
FIS structure contains both the model structure, (which specifies such items as
the number of rules in the FIS, the number of membership functions for each
input, etc.), and the parameters, (which specify the shapes of membership
functions). There are two methods that anfis learning employs for updating
membership function parameters: backpropagation for all parameters (a
steepest descent method), and a hybrid method consisting of backpropagation
for the parameters associated with the input membership functions, and least
squares estimation for the parameters associated with the output membership
functions. As a result, the training error decreases, at least locally, throughout
the learning process. Therefore, the more the initial membership functions
resemble the optimal ones, the easier it will be for the model parameter
training to converge. Human expertise about the target system to be modeled
may aid in setting up these initial membership function parameters in the FIS
structure.
Note that genfis1 produces an FIS structure based on a fixed number of
membership functions. This invokes the so-called curse of dimensionality, and
causes an explosion of the number of rules when the number of inputs is
moderately large, that is, more than four or five. The Fuzzy Logic Toolbox offers
a method that will provide for some dimension reduction in the fuzzy inference
system: you can generate an FIS structure using the clustering algorithm
discussed in “Subtractive Clustering” on page 2-123. From the ANFIS Editor
GUI, this algorithm is selected with a radio button before the FIS is generated.
This subtractive clustering method partitions the data into groups called
clusters, and generates an FIS with the minimum number rules required to
distinguish the fuzzy qualities associated with each of the clusters.
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Training Options
The ANFIS Editor GUI tool allows you to choose your desired error tolerance
and number of training epochs.
Training option trnOpt for the command line anfis is a vector that specifies
the stopping criteria and the step-size adaptation strategy:
• trnOpt(1): number of training epochs, default = 10.
• trnOpt(2): error tolerance, default = 0.
• trnOpt(3): initial step-size, default= 0.01.
• trnOpt(4): step-size decrease rate, default = 0.9.
• trnOpt(5): step-size increase rate, default = 1.1.
If any element of trnOpt is an NaN or missing, then the default value is taken.
The training process stops if the designated epoch number is reached or the
error goal is achieved, whichever comes first.
Usually we want the step-size profile to be a curve that increases initially,
reaches some maximum, and then decreases for the remainder of the training.
This ideal step-size profile is achieved by adjusting the initial step-size and the
increase and decrease rates (trnOpt(3) - trnOpt(5)). The default values are
set up to cover a wide range of learning tasks. For any specific application, you
may want to modify these step-size options in order to optimize the training.
However, as we mentioned previously, there are no user-specified step-size
options for training the adaptive neuro fuzzy inference system generated using
the ANFIS Editor GUI.
Display Options
Display options only apply to the command line function, anfis.
For the command line anfis, the display options argument, dispOpt, is a vector
of either ones or zeros that specifies what information to display, (print in the
MATLAB command line window), before, during, and after the training
process. One is used to denote print this option, whereas zero denotes don’t
print this option.
• dispOpt(1): display ANFIS information, default = 1.
• dispOpt(2): display error (each epoch), default = 1.
• dispOpt(3): display step-size (each epoch), default = 1.
• dispOpt(4): display final results, default = 1.
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The default mode is displays all available information. If any element of
dispOpt is NaN or missing, the default value will be taken.
Method
Both the ANFIS Editor GUI and the command line anfis apply either a
backpropagation form of the steepest descent method for membership function
parameter estimation, or a combination of backpropagation and the
least-squares method to estimate membership function parameters. The
choices for this argument are hybrid or backpropagation. These method
choices are designated in the command line function, anfis, by 1 and 0,
respectively.
Output FIS Structure for Training Data
fismat1 is the output FIS structure corresponding to a minimal training error.
This is the FIS structure that you will use to represent the fuzzy system when
there is no checking data used for model crossvalidation. This data also
represents the FIS structure that is saved by the ANFIS Editor GUI when the
checking data option is not used.
When the checking data option is used, the output saved is that associated with
the minimum checking error.
Training Error
The training error is the difference between the training data output value,
and the output of the fuzzy inference system corresponding to the same
training data input value, (the one associated with that training data output
value). The training error trnError records the root mean squared error
(RMSE) of the training data set at each epoch. fismat1 is the snapshot of the
FIS structure when the training error measure is at its minimum. The ANFIS
Editor GUI will plot the training error vs. epochs curve as the system is
trained.
Step-size
You cannot control the step-size options with the ANFIS Editor GUI. Using the
command line anfis, the step-size array ss records the step-size during the
training. Plotting ss gives the step-size profile, which serves as a reference for
adjusting the initial step-size and the corresponding decrease and increase
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rates. The step-size (ss) for the command line function anfis is updated
according to the following guidelines:
• If the error undergoes four consecutive reductions, increase the step-size by
multiplying it by a constant (ssinc) greater than one.
• If the error undergoes two consecutive combinations of one increase and one
reduction, decrease the step-size by multiplying it by a constant (ssdec) less
than one.
The default value for the initial step-size is 0.01; the default values for ssinc
and ssdec are 1.1 and 0.9, respectively. All the default values can be changed
via the training option for the command line anfis.
Checking Data
The checking data, chkData, is used for testing the generalization capability of
the fuzzy inference system at each epoch. The checking data has the same
format as that of the training data, and its elements are generally distinct from
those of the training data.
The checking data is important for learning tasks for which the input number
is large, and/or the data itself is noisy. In general we want a fuzzy inference
system to track a given input/output data set well. Since the model structure
used for anfis is fixed, there is a tendency for the model to overfit the data on
which is it trained, especially for a large number of training epochs. If
overfitting does occur, we cannot expect the fuzzy inference system to respond
well to other independent data sets, especially if they are corrupted by noise. A
validation or checking data set can be useful for these situations. This data set
is used to crossvalidate the fuzzy inference model. This crossvalidation is
accomplished by applying the checking data to the model, and seeing how well
the model responds to this data.
When the checking data option is used with anfis, either via the command
line, or using the ANFIS Editor GUI, the checking data is applied to the model
at each training epoch. When the command line anfis is invoked, the model
parameters that correspond to the minimum checking error are returned via
the output argument fismat2. The FIS membership function parameters
computed using the ANFIS Editor GUI when both training and checking data
are loaded are associated with the training epoch that has a minimum checking
error.
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The use of the minimum checking data error epoch to set the membership
function parameters assumes
• The checking data is similar enough to the training data that the checking
data error will decrease as the training begins
• The checking data increases at some point in the training, after which data
overfitting has occurred.
As discussed in “ANFIS Editor GUI Example 2: Checking Data Doesn’t
Validate Model” on page 2-106, depending on the behavior of the checking data
error, the resulting FIS may or may not be the one you should be using.
Output FIS Structure for Checking Data
The output of the command line anfis, fismat2, is the output FIS structure
with the minimum checking error. This is the FIS structure that should be
used for further calculation if checking data is used for cross validation.
Checking Error
The checking error is the difference between the checking data output value,
and the output of the fuzzy inference system corresponding to the same
checking data input value, (the one associated with that checking data output
value). The checking error chkError records the RMSE for the checking data
at each epoch. fismat2 is the snapshot of the FIS structure when the checking
error is at its minimum. The ANFIS Editor GUI will plot the checking error vs.
epochs curve as the system is trained.
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Fuzzy Clustering
Clustering of numerical data forms the basis of many classification and system
modeling algorithms. The purpose of clustering is to identify natural groupings
of data from a large data set to produce a concise representation of a system’s
behavior. The Fuzzy Logic Toolbox is equipped with some tools that allow you
to find clusters in input-output training data. You can use the cluster
information to generate a Sugeno-type fuzzy inference system that best models
the data behavior using a minimum number of rules. The rules partition
themselves according to the fuzzy qualities associated with each of the data
clusters. This type of FIS generation can be accomplished automatically using
the command line function, genfis2.
Fuzzy C-Means Clustering
Fuzzy c-means (FCM) is a data clustering technique wherein each data point
belongs to a cluster to some degree that is specified by a membership grade.
This technique was originally introduced by Jim Bezdek in 1981 [Bez81] as an
improvement on earlier clustering methods. It provides a method of how to
group data points that populate some multidimensional space into a specific
number of different clusters?
The Fuzzy Logic Toolbox command line function fcm starts with an initial
guess for the cluster centers, which are intended to mark the mean location of
each cluster. The initial guess for these cluster centers is most likely incorrect.
Additionally, fcm assigns every data point a membership grade for each
cluster. By iteratively updating the cluster centers and the membership grades
for each data point, fcm iteratively moves the cluster centers to the “right”
location within a data set. This iteration is based on minimizing an objective
function that represents the distance from any given data point to a cluster
center weighted by that data point’s membership grade.
fcm is a command line function whose output is a list of cluster centers and
several membership grades for each data point. You can use the information
returned by fcm to help you build a fuzzy inference system by creating
membership functions to represent the fuzzy qualities of each cluster.
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An Example: 2-D Clusters
Let’s use some quasi-random two-dimensional data to illustrate how FCM
clustering works. Load a data set and take a look at it.
load fcmdata.dat
plot(fcmdata(:,1),fcmdata(:,2),'o')
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Now we invoke the command line function, fcm, and ask it to find two clusters
in this data set
[center,U,objFcn] = fcm(fcmdata,2);
Iteration count = 1, obj. fcn = 8.941176
Iteration count = 2, obj. fcn = 7.277177
and so on until the objective function is no longer decreasing much at all.
The variable center contains the coordinates of the two cluster centers, U
contains the membership grades for each of the data points, and objFcn
contains a history of the objective function across the iterations.
The fcm function is an iteration loop built on top of several other routines,
namely initfcm, which initializes the problem, distfcm, which is used for
distance calculations, and stepfcm, which steps through one iteration.
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Plotting the objective function shows the progress of the clustering.
plot(objFcn)
objective function values
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Finally here is a plot displaying the two separate clusters as classified by the
fcm routine. The following figure is generated using:
load fcmdata.dat
[center, U, obj_fcn] = fcm(fcmdata, 2);
maxU = max(U);
index1 = find(U(1, :) == maxU);
index2 = find(U(2, :) == maxU);
line(fcmdata(index1, 1), fcmdata(index1, 2), 'linestyle',...
'none','marker', 'o','color','g');
line(fcmdata(index2,1),fcmdata(index2,2),'linestyle',...
'none','marker', 'x','color','r');
hold on
plot(center(1,1),center(1,2),'ko','markersize',15,'LineWidth',2)
plot(center(2,1),center(2,2),'kx','markersize',15,'LineWidth',2)
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Cluster centers are indicated in the figure below by the large characters.
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Subtractive Clustering
Suppose we don’t have a clear idea how many clusters there should be for a
given set of data. Subtractive clustering, [Chi94], is a fast, one-pass algorithm
for estimating the number of clusters and the cluster centers in a set of data.
The cluster estimates obtained from the subclust function can be used to
initialize iterative optimization-based clustering methods (fcm) and model
identification methods (like anfis). The subclust function finds the clusters by
using the subtractive clustering method.
The genfis2 function builds upon the subclust function to provide a fast,
one-pass method to take input-output training data and generate a
Sugeno-type fuzzy inference system that models the data behavior.
An Example: Suburban Commuting
In this example we apply the genfis2 function to model the relationship
between the number of automobile trips generated from an area and the area’s
demographics. Demographic and trip data are from 100 traffic analysis zones
in New Castle County, Delaware. Five demographic factors are considered:
population, number of dwelling units, vehicle ownership, median household
income, and total employment. Hence the model has five input variables and
one output variable.
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Load the data by typing
tripdata
subplot(2,1,1), plot(datin)
subplot(2,1,2), plot(datout)
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tripdata creates several variables in the workspace. Of the original 100 data
points, we will use 75 data points as training data (datin and datout) and 25
data points as checking data, (as well as for test data to validate the model).
The checking data input/output pairs are denoted by chkdatin and chkdatout.
The genfis2 function generates a model from data using clustering, and
requires you to specify a cluster radius. The cluster radius indicates the range
of influence of a cluster when you consider the data space as a unit hypercube.
Specifying a small cluster radius will usually yield many small clusters in the
data, (resulting in many rules). Specifying a large cluster radius will usually
yield a few large clusters in the data, (resulting in fewer rules). The cluster
radius is specified as the third argument of genfis2. Here we call the genfis2
function using a cluster radius of 0.5.
fismat=genfis2(datin,datout,0.5);
genfis2 is a fast, one-pass method that does not perform any iterative
optimization. An FIS structure is returned; the model type for the FIS
structure is a first order Sugeno model with three rules. We can use evalfis to
verify the model.
fuzout=evalfis(datin,fismat);
trnRMSE=norm(fuzout–datout)/sqrt(length(fuzout))
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trnRMSE =
0.5276
The variable trnRMSE is the root mean square error of the system generated by
the training data. To validate the generalizability of the model, we apply test
data to the FIS. For this example, we use the checking data for both checking
and testing the FIS parameters.
chkfuzout=evalfis(chkdatin,fismat);
chkRMSE=norm(chkfuzout–chkdatout)/sqrt(length(chkfuzout))
chkRMSE =
0.6170
Not surprisingly, the model doesn’t do quite as good a job on the testing data.
A plot of the testing data reveals the difference.
plot(chkdatout)
hold on
plot(chkfuzout,'o')
hold off
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At this point, we can use the optimization capability of anfis to improve the
model. First, we will try using a relatively short anfis training (50 epochs)
without implementing the checking data option, but test the resulting FIS
model against the test data. The command line version of this is as follows:
fismat2=anfis([datin datout],fismat,[50 0 0.1]);
After the training is done, we type:
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fuzout2=evalfis(datin,fismat2);
trnRMSE2=norm(fuzout2–datout)/sqrt(length(fuzout2))
trnRMSE2 =
0.3407
chkfuzout2=evalfis(chkdatin,fismat2);
chkRMSE2=norm(chkfuzout2–chkdatout)/sqrt(length(chkfuzout2))
chkRMSE2 =
0.5827
The model has improved a lot with respect to the training data, but only a little
with respect to the checking data. Here is a plot of the improved testing data.
plot(chkdatout)
hold on
plot(chkfuzout2,'o')
hold off
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Here we see that genfis2 can be used as a stand-alone, fast method for
generating a fuzzy model from data, or as a pre-processor to anfis for
determining the initial rules. An important advantage of using a clustering
method to find rules is that the resultant rules are more tailored to the input
data than they are in an FIS generated without clustering. This reduces the
problem of combinatorial explosion of rules when the input data has a high
dimension (the dreaded curse of dimensionality).
Overfitting
Now let’s consider what happens if we carry out a longer (200 epoch) training
of this system using anfis, including its checking data option.
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[fismat3,trnErr,stepSize,fismat4,chkErr]= ...
anfis([datin datout],fismat2,[200 0
0.1],[], ...
[chkdatin chkdatout]);
The long list of output arguments returns a history of the step-sizes, the RMSE
versus the training data, and the RMSE versus the checking data associated
with each training epoch.
ANFIS training completed at epoch 200.
Minimal training RMSE = 0.326566
Minimal checking RMSE = 0.582545
This looks good. The error with the training data is the lowest we’ve seen, and
the error with the checking data is also lower than before, though not by much.
This suggests that maybe we had gotten about as close as possible with this
system already. Maybe we have even gone so far as to overfit the system to the
training data. Overfitting occurs when we fit the fuzzy system to the training
data so well that it no longer does a very good job of fitting the checking data.
The result is a loss of generality. A look at the error history against both the
training data and the checking data reveals much.
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Training Error
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Here we can see that the training error settles at about the 50th epoch point. In
fact, the smallest value of the checking data error occurs at epoch 52, after
which it increases slightly, even as anfis continues to minimize the error
against the training data all the way to epoch 200. Depending on the specified
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error tolerance, this plot also indicates the model’s ability to generalize the test
data.
A Clustering GUI Tool
There is also the Clustering GUI, which implements fcm and subclust, along
with all of their options. Its use is fairly self-evident.
The clustering GUI looks like this, and is invoked using the command line
function, findcluster.
Load a data set (*.dat)
into your directory.
Choose fcm
or subtractive
clustering method.
Choose two of your data
variables to be plotted on
the screen. Once the data
is loaded, select them
with the pull-down tabs.
Options change
with method
Start clustering
the data.
Save the value of
the cluster center.
You can invoke findcluster with a data set directly, in order to open the GUI
with a data set. The data set must have the extension .dat. For example, to
load the data set, clusterdemo.dat, type findcluster('clusterdemo.dat').
You use the pull-down tab under Method to change between fcm (fuzzy
c-means) and subtractiv (subtractive clustering). More information on the
options can be found in the entries for fcm on page 3-22, and subclust on page
3-72, respectively.
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The Clustering GUI works on multidimensional data sets, but only displays
two of those dimensions. Use the pull-down tabs under X-axis and Y-axis to
select which data dimension you want to view.
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Stand-Alone C-Code Fuzzy Inference Engine
In the fuzzy/fuzzy directory of the toolbox, you can find two C files, fismain.c
and fis.c, which are provided as the source codes for a stand-alone fuzzy
inference engine. The stand-alone C-code fuzzy inference engine can read an
FIS file and an input data file to perform fuzzy inference directly, or it can be
embedded in other external applications.
To compile the stand-alone fuzzy inference engine on a UNIX system, type
% cc –O –o fismain fismain.c –lm
(Note that % is only symbolic of a UNIX prompt, and that you do not have to
type fis.c explicitly, since it is included in fismain.c.) Upon successful
compilation, type the executable command to see how it works:
% fismain
This prompts the following message:
% Usage: fismain data_file fis_file
This means that fismain needs two files to do its work: a data file containing
rows of input vectors, and an FIS file that specifies the fuzzy inference system
under consideration.
For example, consider an FIS structure file named, mam21.fis. We can prepare
the input data file using MATLAB:
[x, y] = meshgrid(–5:5, –5:5);
input_data = [x(:) y(:)];
save fis_in input_data –ascii
This saves all the input data as a 121-by-2 matrix in the ASCII file fis_in,
where each row of the matrix represents an input vector.
Now we can call the stand-alone code:
% fismain fis_in mam21.fis
This generates 121 outputs on your screen. You can direct the outputs to
another file:
% fismain fis_in mam21.fis > fis_out
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Now the file fis_out contains a 121-by-1 matrix. In general, each row of the
output matrix represents an output vector. The syntax of fismain is similar to
its MEX-file counterpart evalfis.m, except that all matrices are replaced with
files.
To compare the results from the MATLAB MEX-file and the stand-alone
executable, type the following within MATLAB:
fismat = readfis('mam21');
matlab_out = evalfis(input_data, fismat);
load fis_out
max(max(matlab_out – fis_out))
ans =
4.9583e–13
This tiny difference comes from the limited length printout in the file fis_out.
There are several things you should know about this stand-alone executable:
• It is compatible with both ANSI and K & R standards for C code, as long as
__STDC__ is defined in ANSI compilers.
• Customized functions are not allowed in the stand-alone executable, so you
are limited to the 11 membership functions that come with the toolbox, as
well as other factory settings for AND, OR, IMP, and AGG functions.
• fismain.c contains only the main() function and it is heavily documented
for easy adaptation to other applications.
• To add a new membership function or new reasoning mechanism into the
stand-alone code, you need to change the file fis.c, which contains all the
necessary functions to perform the fuzzy inference process.
• For the Macintosh, the compiled command fismain tries to find fismain.in
and fismain.fis as input data and FIS description files, respectively. The
output is stored in fismain.out. These filenames are defined within
Macintosh-specific #define symbols in fismain.c and can be changed if
necessary.
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Glossary
This section is designed to briefly explain some of the specialized terms that are
derived from fuzzy logic.
aggregation - the combination of the consequents of each rule in a Mamdani
fuzzy inference system in preparation for defuzzification.
Adaptive Neuro-Fuzzy Inference System (ANFIS) - a technique for
automatically tuning Sugeno-type inference systems based on training data.
antecedent - the initial (or “if”) part of a fuzzy rule.
consequent - the final (or “then”) part of a fuzzy rule.
defuzzification - the process of transforming a fuzzy output of a fuzzy
inference system into a crisp output.
degree of membership - the output of a membership function, this value is
always limited to between 0 and 1. Also known as a membership value or
membership grade.
degree of fulfillment - see firing strength.
firing strength - the degree to which the antecedent part of a fuzzy rule is
satisfied. The firing strength may be the result of an AND or an OR operation,
and it shapes the output function for the rule. Also known as degree of
fulfillment.
fuzzification - the process of generating membership values for a fuzzy
variable using membership functions.
fuzzy c-means clustering - a data clustering technique wherein each data
point belongs to a cluster to a degree specified by a membership grade.
fuzzy inference system (FIS) - the overall name for a system that uses fuzzy
reasoning to map an input space to an output space.
fuzzy operators - AND, OR, and NOT operators. These are also known as
logical connectives.
fuzzy set - a set which can contain elements with only a partial degree of
membership.
fuzzy singleton - a fuzzy set with a membership function that is unity at a one
particular point and zero everywhere else.
2-132
Glossary
implication - the process of shaping the fuzzy set in the consequent based on
the results of the antecedent in a Mamdani-type FIS.
Mamdani-type inference - a type of fuzzy inference in which the fuzzy sets
from the consequent of each rule are combined through the aggregation
operator and the resulting fuzzy set is defuzzified to yield the output of the
system.
membership function (MF) - a function that specifies the degree to which a
given input belongs to a set or is related to a concept.
singleton output function - an output function that is given by a spike at a
single number rather than a continuous curve. In the Fuzzy Logic Toolbox it is
only supported as part of a zero-order Sugeno model.
subtractive clustering - a technique for automatically generating fuzzy
inference systems by detecting clusters in input-output training data.
Sugeno-type inference - a type of fuzzy inference in which the consequent of
each rule is a linear combination of the inputs. The output is a weighted linear
combination of the consequents.
T-conorm - (also known as S-norm) a two-input function that describes a
superset of fuzzy union (OR) operators, including maximum, algebraic sum,
and any of several parameterized T-conorms.
T-norm - a two-input function that describes a superset of fuzzy intersection
(AND) operators, including minimum, algebraic product, and any of several
parameterized T-norms.
2-133
2
Tutorial
References
[Bez81] Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function
Algorithms, Plenum Press, New York, 1981.
[Chi94] Chiu, S., “Fuzzy Model Identification Based on Cluster Estimation,”
Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, Sept. 1994.
[Dub80] Dubois, D. and H. Prade, Fuzzy Sets and Systems: Theory and
Applications, Academic Press, New York, 1980.
[Jan91] Jang, J.-S. R., “Fuzzy Modeling Using Generalized Neural Networks
and Kalman Filter Algorithm,” Proc. of the Ninth National Conf. on Artificial
Intelligence (AAAI-91), pp. 762-767, July 1991.
[Jan93] Jang, J.-S. R., “ANFIS: Adaptive-Network-based Fuzzy Inference
Systems,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No.
3, pp. 665-685, May 1993.
[Jan94] Jang, J.-S. R. and N. Gulley, “Gain scheduling based fuzzy controller
design,” Proc. of the International Joint Conference of the North American
Fuzzy Information Processing Society Biannual Conference, the Industrial
Fuzzy Control and Intelligent Systems Conference, and the NASA Joint
Technology Workshop on Neural Networks and Fuzzy Logic, San Antonio,
Texas, Dec. 1994.
[Jan95] Jang, J.-S. R. and C.-T. Sun, “Neuro-fuzzy modeling and control,”
Proceedings of the IEEE, March 1995.
[Jan97] Jang, J.-S. R. and C.-T. Sun, Neuro-Fuzzy and Soft Computing: A
Computational Approach to Learning and Machine Intelligence, Prentice Hall,
1997.
[Kau85] Kaufmann, A. and M.M. Gupta, Introduction to Fuzzy Arithmetic, V.N.
Reinhold, 1985.
[Lee90] Lee, C.-C., “Fuzzy logic in control systems: fuzzy logic controller-parts
1 and 2,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, No. 2,
pp 404-435, 1990.
[Mam75] Mamdani, E.H. and S. Assilian, “An experiment in linguistic
synthesis with a fuzzy logic controller,” International Journal of Man-Machine
Studies, Vol. 7, No. 1, pp. 1-13, 1975.
2-134
References
[Mam76] Mamdani, E.H., “Advances in the linguistic synthesis of fuzzy
controllers,” International Journal of Man-Machine Studies, Vol. 8, pp.
669-678, 1976.
[Mam77] Mamdani, E.H., “Applications of fuzzy logic to approximate reasoning
using linguistic synthesis,” IEEE Transactions on Computers, Vol. 26, No. 12,
pp. 1182-1191, 1977.
[Sch63] Schweizer, B. and A. Sklar, “Associative functions and abstract
semi-groups,” Publ. Math Debrecen, 10:69-81, 1963.
[Sug77] Sugeno, M., “Fuzzy measures and fuzzy integrals: a survey,” (M.M.
Gupta, G. N. Saridis, and B.R. Gaines, editors) Fuzzy Automata and Decision
Processes, pp. 89-102, North-Holland, New York, 1977.
[Sug85] Sugeno, M., Industrial applications of fuzzy control, Elsevier Science
Pub. Co., 1985.
[Wan94] Wang, L.-X., Adaptive fuzzy systems and control: design and stability
analysis, Prentice Hall, 1994.
[WidS85] Widrow, B. and D. Stearns, Adaptive Signal Processing, Prentice
Hall, 1985.
[Yag80] Yager, R., “On a general class of fuzzy connectives,” Fuzzy Sets and
Systems, 4:235-242, 1980.
[Yag94] Yager, R. and D. Filev, “Generation of Fuzzy Rules by Mountain
Clustering,” Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, pp. 209-219,
1994.
[Zad65] Zadeh, L.A., “Fuzzy sets,” Information and Control, Vol. 8, pp. 338-353,
1965.
[Zad73] Zadeh, L.A., “Outline of a new approach to the analysis of complex
systems and decision processes,” IEEE Transactions on Systems, Man, and
Cybernetics, Vol. 3, No. 1, pp. 28-44, Jan. 1973.
[Zad75] Zadeh, L.A., “The concept of a linguistic variable and its application to
approximate reasoning, Parts 1, 2, and 3,” Information Sciences, 1975,
8:199-249, 8:301-357, 9:43-80
[Zad88] Zadeh, L.A., “Fuzzy Logic,” Computer, Vol. 1, No. 4, pp. 83-93, 1988.
[Zad89] Zadeh, L.A., “Knowledge representation in fuzzy logic,” IEEE
Transactions on Knowledge and Data Engineering, Vol. 1, pp. 89-100, 1989.
2-135
2
Tutorial
2-136
3
Reference
GUI Tools . . . . . . . . . .
Membership Functions . . . .
FIS Data Structure Management
Advanced Techniques . . . . .
Simulink Blocks . . . . . . .
Demos . . . . . . . . . . .
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3-2
3-2
3-3
3-4
3-4
3-5
3
Reference
This section of the chapter contains brief descriptions of all the functions in the
Fuzzy Logic Toolbox. The following tables contain the functions listed by topic.
GUI Tools
Function
Purpose
anfisedit
ANFIS Editor GUI.
fuzzy
Basic FIS Editor.
mfedit
Membership Function Editor.
ruleedit
Rule Editor and parser.
ruleview
Rule Viewer and fuzzy inference diagram.
surfview
Output Surface Viewer.
Membership Functions
3-2
Function
Purpose
dsigmf
Difference of two sigmoid membership functions.
gauss2mf
Two-sided Gaussian curve membership function.
gaussmf
Gaussian curve membership function.
gbellmf
Generalized bell curve membership function.
pimf
Pi-shaped curve membership function.
Function
Purpose
psigmf
Product of two sigmoidal membership functions.
smf
S-shaped curve membership function.
sigmf
Sigmoid curve membership function.
trapmf
Trapezoidal membership function.
trimf
Triangular membership function.
zmf
Z-shaped curve membership function.
FIS Data Structure Management
Function
Purpose
addmf
Add membership function to FIS.
addrule
Add rule to FIS.
addvar
Add variable to FIS.
defuzz
Defuzzify membership function.
evalfis
Perform fuzzy inference calculation.
evalmf
Generic membership function evaluation.
gensurf
Generate FIS output surface.
getfis
Get fuzzy system properties.
mf2mf
Translate parameters between functions.
newfis
Create new FIS.
parsrule
Parse fuzzy rules.
plotfis
Display FIS input-output structure.
plotmf
Plot all of the membership functions associated
with a given variable.
3-3
3
Reference
Function
Purpose
readfis
Load FIS from disk.
rmmf
Remove membership function from FIS.
rmvar
Remove variable from FIS.
setfis
Set fuzzy system properties.
showfis
Display annotated FIS.
showrule
Display FIS rules.
writefis
Save FIS to disk.
Advanced Techniques
Function
Purpose
anfis
Training routine for a Sugeno-type FIS (MEX only).
fcm
Find clusters with FCM clustering.
genfis1
Generate FIS matrix using grid method.
genfis2
Generate FIS matrix using subtractive clustering.
subclust
Find cluster centers with subtractive clustering.
Simulink Blocks
3-4
Function
Purpose
fuzblock
Fuzzy logic controller blocks and demo blocks.
sffis
Fuzzy inference S-function.
Demos
Function
Purpose
defuzzdm
Defuzzification methods.
fcmdemo
FCM clustering demo (2-D).
fuzdemos
GUI for Fuzzy Logic Toolbox demos.
gasdemo
ANFIS demo for fuel efficiency using subclustering.
juggler
Ball-juggler with Rule Viewer.
invkine
Inverse kinematics of a robot arm.
irisfcm
FCM clustering demo (4-D).
noisedm
Adaptive noise cancellation.
slbb
Ball and beam control (Simulink ).
slcp
Inverted pendulum control (Simulink ).
sltank
Water level control (Simulink).
sltankrule
Water level control with Rule Viewer (Simulink).
sltbu
Truck backer-upper (Simulink only).
3-5
addmf
Purpose
3addmf
Add a membership function to an FIS.
Synopsis
a = addmf(a,'varType',varIndex,'mfName','mfType',mfParams)
Description
A membership function can only be added to a variable in an existing MATLAB
workspace FIS. Indices are assigned to membership functions in the order in
which they are added, so the first membership function added to a variable will
always be known as membership function number one for that variable. You
cannot add a membership function to input variable number two of a system if
only one input has been defined.
The function requires six input arguments in this order:
1 A MATLAB variable name of a FIS structure in the workspace
2 A string representing the type of variable you want to add the membership
function to ('input' or 'output')
3 The index of the variable you want to add the membership function to
4 A string representing the name of the new membership function
5 A string representing the type of the new membership function
6 The vector of parameters that specify the membership function
3-6
addmf
Example
a=newfis('tipper');
a=addvar(a,'input','service',[0 10]);
a=addmf(a,'input',1,'poor','gaussmf',[1.5 0]);
a=addmf(a,'input',1,'good','gaussmf',[1.5 5]);
a=addmf(a,'input',1,'excellent','gaussmf',[1.5 10]);
plotmf(a,'input',1)
poor
good
excellent
Degree of belief
1
0.8
0.6
0.4
0.2
0
0
See Also
1
2
3
4
5
service
6
7
8
9
10
addrule, addvar, plotmf, rmmf, rmvar
3-7
addrule
Purpose
3addrule
Add a rule to an FIS.
Synopsis
a = addrule(a,ruleList)
Description
addrule has two arguments. The first argument is the MATLAB workspace
variable FIS name. The second argument for addrule is a matrix of one or more
rows, each of which represents a given rule. The format that the rule list matrix
must take is very specific. If there are m inputs to a system and n outputs, there
must be exactly m + n + 2 columns to the rule list.
The first m columns refer to the inputs of the system. Each column contains a
number that refers to the index of the membership function for that variable.
The next n columns refer to the outputs of the system. Each column contains a
number that refers to the index of the membership function for that variable.
The m + n + 1 column contains the weight that is to be applied to the rule. The
weight must be a number between zero and one, and is generally left as one.
The m + n + 2 column contains a 1 if the fuzzy operator for the rule’s antecedent
is AND. It contains a 2 if the fuzzy operator is OR.
Example
ruleList=[
1 1 1 1 1
1 2 2 1 1];
a = addrule(a,ruleList);
If the above system a has two inputs and one output, the first rule can be
interpreted as: “If input 1 is MF 1 and input 2 is MF 1, then output 1 is MF 1.”
See Also
3-8
addmf, addvar, rmmf, rmvar, parsrule, showrule
addvar
Purpose
3addvar
Add a variable to an FIS.
Synopsis
a = addvar(a,'varType','varName',varBounds)
Description
addvar has four arguments in this order:
1 The name of a FIS structure in the MATLAB workspace
2 A string representing the type of the variable you want to add ('input' or
'output')
3 A string representing the name of the variable you want to add
4 The vector describing the limiting range values for the variable you want to
add
Indices are applied to variables in the order in which they are added, so the
first input variable added to a system will always be known as input variable
number one for that system. Input and output variables are numbered
independently.
Example
a=newfis('tipper');
a=addvar(a,'input','service',[0 10]);
getfis(a,'input',1)
MATLAB replies
Name = service
NumMFs = 0
MFLabels =
Range = [0 10]
See Also
addmf, addrule, rmmf, rmvar
3-9
anfis
Purpose
3anfis
Training routine for Sugeno-type FIS (MEX only).
Synopsis
[fismat,error1,stepsize] = anfis(trnData)
[fismat,error1,stepsize] = anfis(trnData,fismat)
[fismat1,error1,stepsize] = ...
anfis(trnData,fismat,trnOpt,dispOpt)
[fismat1,error1,stepsize,fismat2,error2] = ...
anfis(trnData,trnOpt,dispOpt,chkData)
[fismat1,error1,stepsize,fismat2,error2] = ...
anfis(trnData,trnOpt,dispOpt,chkData,optMethod)
Description
This is the major training routine for Sugeno-type fuzzy inference systems.
anfis uses a hybrid learning algorithm to identify parameters of Sugeno-type
fuzzy inference systems. It applies a combination of the least-squares method
and the backpropagation gradient descent method for training FIS
membership function parameters to emulate a given training data set. anfis
can also be invoked using an optional argument for model validation. The type
of model validation that takes place with this option is a checking for model
overfitting, and the argument is a data set called the checking data set.
The arguments in the above description for anfis are as follows:
• trnData: the name of a training data set. This is a matrix with all but the
last column containing input data, while the last column contains a single
vector of output data.
• fismat: the name of an FIS, (fuzzy inference system) used to provide anfis
with an initial set of membership functions for training. Without this option,
anfis will use genfis1 to implement a default initial FIS for training. This
default FIS will have two membership functions of the Gaussian type, when
invoked with only one argument. If fismat is provided as a single number (or
a vector), it is taken as the number of membership functions (or the vector
whose entries are the respective numbers of membership functions
associated with each respective input when these numbers differ for each
input). In this case, both arguments of anfis are passed to genfis1 to
generate a valid FIS structure before starting the training process.
3-10
anfis
• trnOpt: vector of training options. When any training option is entered as
NaN the default options will be in force. These options are as follows:
trnOpt(1): training epoch number (default: 10)
trnOpt(2): training error goal (default: 0)
trnOpt(3): initial step size (default: 0.01)
trnOpt(4): step size decrease rate (default: 0.9)
trnOpt(5): step size increase rate (default: 1.1)
• dispOpt: vector of display options that specify what message to display in the
MATLAB command window during training. The default value for any
display option is 1, which means the corresponding information is displayed.
A 0 means the corresponding information is not displayed on the screen.
When any display option is entered as NaN, the default options will be in
force. These options are as follows:
dispOpt(1): ANFIS information, such as numbers of input and output
membership functions, and so on (default: 1)
dispOpt(2): error (default: 1)
dispOpt(3): step size at each parameter update (default: 1)
dispOpt(4): final results (default: 1)
• chkData: the name of an optional checking data set for overfitting model
validation. This data set is a matrix in the same format as the training data
set.
• optMethod: optional optimization method used in membership function
parameter training: either 1 for the hybrid method or 0 for the
backpropagation method. The default method is the hybrid method, which is
a combination of least squares estimation with backpropagation. The default
method is invoked whenever the entry for this argument is anything but 0.
The training process stops whenever the designated epoch number is reached
or the training error goal is achieved.
3-11
anfis
Note on anfis arguments: When anfis is invoked with two or more
arguments, any optional arguments will take on their default values if they
are entered as NaNs or empty matrices. Default values can be changed directly
by modifying the file anfis.m. Either NaNs or empty matrices must be used as
place-holders for variables if you don’t want to specify them, but do want to
specify succeeding arguments, for example, when you implement the checking
data option of anfis.
The range variables in the above description for anfis are as follows:
• fismat1 is the FIS structure whose parameters are set according to a
minimum training error criterion.
• error1 or error2 is an array of root mean squared errors representing the
training data error signal and the checking data error signal, respectively.
• stepsize is an array of step sizes. The step size is decreased (by multiplying
it with the component of the training option corresponding to the step size
decrease rate) if the error measure undergoes two consecutive combinations
of an increase followed by a decrease. The step size is increased (by
multiplying it with the increase rate) if the error measure undergoes four
consecutive decreases.
• fismat2 is the FIS structure whose parameters are set according to a
minimum checking error criterion.
3-12
anfis
Example
x = (0:0.1:10)';
y = sin(2*x)./exp(x/5);
trnData = [x y];
numMFs = 5;
mfType = 'gbellmf';
epoch_n = 20;
in_fismat = genfis1(trnData,numMFs,mfType);
out_fismat = anfis(trnData,in_fismat,20);
plot(x,y,x,evalfis(x,out_fismat));
legend('Training Data','ANFIS Output');
See Also
genfis1, anfis
References
Jang, J.-S. R., “Fuzzy Modeling Using Generalized Neural Networks and
Kalman Filter Algorithm,” Proc. of the Ninth National Conf. on Artificial
Intelligence (AAAI-91), pp. 762-767, July 1991.
Jang, J.-S. R., “ANFIS: Adaptive-Network-based Fuzzy Inference Systems,”
IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp.
665-685, May 1993.
3-13
anfisedit
Purpose
Synopsis
3anfisedit
To open the ANFIS Editor GUI.
anfisedit('a')
anfisedit(a)
anfisedit
Description
Load or save fuzzy
Sugeno system or open
new Sugeno system.
Undo
Open FIS or Edit with any
of the other GUIs.
Plot region
Status of the number of inputs,
outputs, input membership functions
and output membership functions
Testing Data appears on the
plot in blue as ...
Training Data appears on
the plot in blue as o o o
After you generate
or load a FIS, this
button allows you to
open a graphical
representation of its
input/output
structure.
Checking Data appears on
the plot in blue as +++
FIS Output appears on the
screen in red as ***
Load either training,
testing, or checking
data from disk or
workspace, or load
demo data. Data
appears in the plot
region.
Test data against
the FIS model. The
plot appears in the
plot region.
Clear Data unloads a loaded data
set checked under Type:.
The plot region is cleared even if other
data types are still loaded.
Load FIS or generate FIS
from loaded data using
your chosen number of MFs
and rules or fuzzy.
Train FIS after setting optimization
method, error tolerance, and number
of epochs. This generates error plots
in the plot region.
Using anfisedit, you bring up the ANFIS Editor GUI from which you can load
a data set and train anfis. The ANFIS Editor GUI invoked using
3-14
anfisedit
anfisedit('a'), brings up the ANFIS Editor GUI from which you can
implement anfis using a FIS structure stored as a file on your disk called,
a.fis.
anfisedit(a) operates the same way for a FIS structure a, stored as a variable
in the MATLAB workspace.
Refer to “anfis and the ANFIS Editor GUI” on page 2-92 for more information
about how to use anfisedit.
Menu Items
On the ANFIS Editor GUI, there is a menu bar that allows you to open related
GUI tools, open and save systems, and so on. The File menu is the same as the
one found on the FIS Editor. Refer to fuzzy on page 3-29 for more information.
• Use the following Edit menu item:
Undo to undo the most recent change.
• Use the following View menu items:
Edit FIS properties... to invoke the FIS Editor.
Edit rules... to invoke the Rule Editor.
Edit membership functions... to invoke the Membership Function Editor.
View rules... to invoke the Rule Viewer.
View surface... to invoke the Surface Viewer.
See Also
fuzzy, mfedit, ruleedit, ruleview, surfview
3-15
convertfis
Purpose
Synopsis
Description
3convertfis
Convert a Fuzzy Logic Toolbox version 1.0 FIS matrix to a version 2.0 FIS
structure.
fis_new=convertfis(fis_old)
convertfis takes a version 1.0 FIS matrix and converts it to a version 2.0
structure.
3-16
defuzz
Purpose
3defuzz
Defuzzify membership function.
Synopsis
out = defuzz(x,mf,type)
Description
defuzz(x,mf,type) returns a defuzzified value out, of a membership function
mf positioned at associated variable value x, using one of several defuzzification
strategies, according to the argument, type. The variable type can be one of the
following.
• centroid: centroid of area method
• bisector: bisector of area method
• mom: mean of maximum method
• som: smallest of maximum method
• lom: largest of maximum method
If type is not one of the above, it is assumed to be a user-defined function. x and
mf are passed to this function to generate the defuzzified output.
Examples
x = −10:0.1:10;
mf = trapmf(x,[−10 −8 −4 7]);
xx = defuzz(x,mf,'centroid');
3-17
dsigmf
Purpose
3dsigmf
Built-in membership function composed of the difference between two
sigmoidal membership functions.
Synopsis
y = dsigmf(x,[a1 c1 a2 c2])
Description
The sigmoidal membership function used here depends on the two parameters
a and c and is given by
1
f ( x ;a, c ) = -----------------------------–a ( x – c )
1+e
The membership function dsigmf depends on four parameters, a1, c1, a2, and
c2, and is the difference between two of these sigmoidal functions:
f1(x; a1, c1) - f2(x; a2, c2)
The parameters are listed in the order: [a1 c1 a2 c2].
Example
x=0:0.1:10;
y=dsigmf(x,[5 2 5 7]);
plot(x,y)
xlabel('dsigmf, P=[5 2 5 7]')
1
0.75
0.5
0.25
0
0
See Also
3-18
2
4
6
dsigmf, P = [5 2 5 7]
8
10
gaussmf, gauss2mf, gbellmf, evalmf, mf2mf, pimf, psigmf, sigmf, smf, trapmf,
trimf, zmf
evalfis
Purpose
3evalfis
Perform fuzzy inference calculations.
Synopsis
output= evalfis(input,fismat)
output= evalfis(input,fismat, numPts)
[output, IRR, ORR, ARR]= evalfis(input,fismat)
[output, IRR, ORR, ARR]= evalfis(input,fismat, numPts)
Description
evalfis has the following arguments:
• input: a number or a matrix specifying input values. If input is an M-by-N
matrix, where N is number of input variables, then evalfis takes each row
of input as an input vector and returns the M-by-L matrix to the variable,
output, where each row is an output vector and L is the number of output
variables.
• fismat: an FIS structure to be evaluated.
• numPts: an optional argument that represents the number of sample points
on which to evaluate the membership functions over the input or output
range. If this argument is not used, the default value of 101 point is used.
The range labels for evalfis are as follows:
• output: the output matrix of size M-by-L, where M represents the number of
input values specified above, and L is the number of output variables for the
FIS.
The optional range variables for evalfis are only calculated when the input
argument is a row vector, (only one set of inputs is applied). These optional
range variables are:
• IRR: the result of evaluating the input values through the membership
functions. This is a matrix of size numRules-by-N, where numRules is the
number of rules, and N is the number of input variables.
• ORR: the result of evaluating the output values through the membership
functions. This is a matrix of size numPts-by-numRules*L, where numRules
is the number of rules, and L is the number of outputs. The first numRules
columns of this matrix correspond to the first output, the next numRules
columns of this matrix correspond to the second output, and so forth.
• ARR: the numPts-by-L matrix of the aggregate values sampled at numPts along
the output range for each output.
3-19
evalfis
When invoked with only one range variable, this function computes the output
vector, output, of the fuzzy inference system specified by the structure,
fismat, for the input value specified by the number or matrix, input.
Example
fismat = readfis('tipper');
out = evalfis([2 1; 4 9],fismat)
This generates the response
out =
7.0169
19.6810
See Also
3-20
ruleview, gensurf
evalmf
Purpose
3evalmf
Generic membership function evaluation.
Synopsis
y = evalmf(x,mfParams,mfType)
Description
evalmf evaluates any membership function, where x is the variable range for
the membership function evaluation, mfType is a membership function from
the toolbox, and mfParams are appropriate parameters for that function.
If you want to create your own custom membership function, evalmf will still
work, because it evaluates any membership function whose name it doesn’t
recognize.
Examples
x=0:0.1:10;
mfparams = [2 4 6];
mftype = 'gbellmf';
y=evalmf(x,mfparams,mftype);
plot(x,y)
xlabel('gbellmf, P=[2 4 6]')
1
0.75
0.5
0.25
0
0
See Also
2
4
6
gbellmf, P = [2 4 6]
8
10
dsigmf, gaussmf, gauss2mf, gbellmf, mf2mf, pimf, psigmf, sigmf, smf, trapmf,
trimf, zmf
3-21
fcm
Purpose
Synopsis
Description
3fcm
Fuzzy c-means clustering.
[center,U,obj_fcn] = fcm(data,cluster_n)
[center, U, obj_fcn] = fcm(data, cluster_n) applies the fuzzy c-means
clustering method to a given data set.
The input arguments of this function are:
• data: data set to be clustered; each row is a sample data point
• cluster_n: number of clusters (greater than one)
The output arguments of this function are:
• center: matrix of final cluster centers where each row provides the center
coordinates
• U: final fuzzy partition matrix (or membership function matrix)
• obj_fcn: values of the objective function during iterations
fcm(data,cluster_n,options) uses an additional argument variable,
options, to control clustering parameters, introduce a stopping criteria, and/
or set the iteration information display:
options(1): exponent for the partition matrix U (default: 2.0)
options(2): maximum number of iterations (default: 100)
options(3): minimum amount of improvement (default: 1e-5)
options(4): info display during iteration (default: 1)
If any entry of options is NaN, the default value for that option is used instead.
The clustering process stops when the maximum number of iterations is
reached, or when the objective function improvement between two consecutive
iterations is less than the minimum amount of improvement specified.
3-22
fcm
Example
data = rand(100, 2);
[center,U,obj_fcn] = fcm(data, 2);
plot(data(:,1), data(:,2),'o');
maxU = max(U);
index1 = find(U(1,:) == maxU);
index2 = find(U(2, :) == maxU);
line(data(index1,1), data(index1, 2), 'linestyle', 'none',
'marker', '*',
'color', 'g');
line(data(index2,1), data(index2, 2), 'linestyle', 'none',
'marker', '*',
'color', 'r');
3-23
findcluster
Purpose
3findcluster
Interactive clustering GUI for fuzzy c-means and subclustering.
Synopsis
findcluster
findcluster('file.dat')
Description
findcluster brings up a GUI to implement fuzzy c-means (fcm) and/or fuzzy
subtractive clustering (subtractiv) using the pull-down tab under Method on
the GUI. Data is entered using the Load Data button. The options for each of
these methods are set to default values. These can be changed. A description of
the options for fuzzy c-means is found in fcm on page 3-22. A description of the
options for fuzzy subclustering is found in subclust on page 3-72.
This tool works on multidimensional data sets, but only displays two of those
dimensions. Use the pull-down tabs under X-axis and Y-axis to select which
data dimension you want to view. For example, if you have data that is
five-dimensional, this tool labels the data as data_1, data_2, data_3, data_4,
data_5, in the order in which the data appears in the data set. Start will
perform the clustering, and Save Center will save the cluster center.
When operating on a data set, file.dat, findcluster (file.dat) loads the
data set automatically, plotting up to the first two dimensions of the data only.
3-24
findcluster
You can still choose which two dimensions of the data you want to cluster after
the GUI comes up.
Example
findcluster('clusterdemo.dat')
See Also
fcm, subclust
3-25
fuzarith
Purpose
3fuzarith
Synopsis
C = fuzarith(X, A, B, operator)
Description
Using interval arithmetic, C = fuzarith(X, A, B, operator) returns a fuzzy set
C as the result of applying the function represented by the string, operator,
that performs a binary operation on the sampled convex fuzzy sets A and B. The
elements of A and B are derived from convex functions of the sampled universe,
X.
To perform fuzzy arithmetic.
• A, B, and X are vectors of the same dimension.
• operator is one of the following strings: 'sum', 'sub', 'prod', and 'div'.
• The returned fuzzy set C is a column vector with the same length as X.
Remark
Example
3-26
Fuzzy addition might generate the message: divide by zero, but this will not
affect the correctness of this function.
point_n = 101;% this determines MF's resolution
min_x = -20; max_x = 20;% universe is [min_x, max_x]
x = linspace(min_x, max_x, point_n)';
A = trapmf(x, [-10 -2 1 3]);% trapezoidal fuzzy set A
B = gaussmf(x, [2 5]);% Gaussian fuzzy set B
C1 = fuzarith(x, A, B, 'sum');
subplot(2,1,1);
plot(x, A, 'b--', x, B, 'm:', x, C1, 'c');
title('fuzzy addition A+B');
C2 = fuzarith(x, A, B, 'sub');
subplot(2,1,2);
plot(x, A, 'b--', x, B, 'm:', x, C2, 'c');
title('fuzzy subtraction A-B');
C3 = fuzarith(x, A, B, 'prod');
fuzblock
Purpose
3fuzblock
Simulink fuzzy logic controller block.
Synopsis
fuzblock
Description
This command brings up a Simulink system that, in addition to some Simulink
demo blocks, contains two Simulink blocks you can use:
• The Fuzzy Logic Controller
• The Fuzzy Logic Controller With Rule Viewer, (see also ruleview on page
3-61). This block forces the Rule Viewer to pop open during a Simulink
simulation.
The dialog box associated with either of these blocks is found by double-clicking
on the Fuzzy Logic Controller block. This box contains the name of the FIS
structure in the workspace that corresponds to the desired fuzzy system you
want in your Simulink model.
To open this dialog box for the Fuzzy Logic Controller With Rule Viewer block,
you have to
1 Double-click on this block, and a Simulink diagram with a Fuzzy Logic
Controller block opens.
2 Double-click on the second Fuzzy Logic Controller block that pops open.
If the fuzzy inference system has multiple inputs, these inputs should be
multiplexed together before feeding them into either the Fuzzy Logic
Controller or the Fuzzy Logic Controller With Rule Viewer block. Similarly, if
the system has multiple outputs, these signals will be passed out of the block
on one multiplexed line.
See Also
sffis, ruleview
3-27
fuzdemos
Purpose
3fuzdemos
List of all Fuzzy Logic Toolbox demos.
Synopsis
fuzdemos
Description
This function brings up a GUI that allows you to choose between any of several
Fuzzy Logic Toolbox demos listed under “Demos” on page 3-5.
3-28
fuzzy
Purpose
Synopsis
3fuzzy
To invoke the basic FIS editor.
fuzzy
fuzzy(fismat)
This GUI tool allows you to edit the highest level features of the fuzzy inference
system, such as the number of input and output variables, the defuzzification
method used, and so on. Refer to “The FIS Editor” on page 2-49 and ff., for more
information about how to use the GUIs associated with fuzzy.
The FIS Editor is the high-level display for any fuzzy logic inference system. It
allows you to call the various other editors to operate on the FIS. This interface
allows convenient access to all other editors with an emphasis on maximum
flexibility for interaction with the fuzzy system.
The Diagram
The diagram displayed at the top of the window shows the inputs, outputs, and
a central fuzzy rule processor. Click on one of the variable boxes to make the
selected box the current variable. You should see the box highlighted in red.
Double-click on one of the variables to bring up the Membership Function
Editor. Double-click on the fuzzy rule processor to bring up the Rule Editor. If
3-29
fuzzy
a variable exists but is not mentioned in the rule base, it is connected to the
rule processor block with a dashed rather than a solid line.
Menu Items
The FIS Editor displays a menu bar that allows you to open related GUI tools,
open and save systems, and so on.
• Under File select:
New Mamdani FIS... to open a new Mamdani-style system with no
variables and no rules called Untitled.
New Sugeno FIS... to open a new Sugeno-style system with no variables
and no rules called Untitled.
Open from disk... to load a system from a specified .fis file on disk.
Save to disk to save the current system to a .fis file on disk.
Save to disk as... to save the current system to disk with the option to
rename or relocate the file.
Open from workspace... to load a system from a specified FIS structure
variable in the workspace.
Save to workspace... to save the system to the currently named FIS
structure variable in the workspace.
Save to workspace as... to save the system to a specified FIS structure
variable in the workspace.
Close window to close the GUI.
• Under Edit select:
Add input to add another input to the current system.
Add output to add another output to the current system.
Remove variable to delete a selected variable.
Undo to undo the most recent change.
• Under View select:
Edit MFs... to invoke the Membership Function Editor.
Edit rules... to invoke the Rule Editor.
Edit anfis... to invoke the ANFIS Editor for single output Sugeno systems
only.
View rules... to invoke the Rule Viewer.
View surface... to invoke the Surface Viewer.
3-30
fuzzy
Inference
Method Pop-up
Menus
Five pop-up menus are provided to change the functionality of the five basic
steps in the fuzzy implication process:
• And method: Choose min, prod, or Custom, for a custom operation.
• Or method: Choose max, probor (probabilistic or), or Custom, for a custom
operation.
• Implication method: Choose min, prod, or Custom, for a custom operation.
This selection is not available for Sugeno-style fuzzy inference.
• Aggregation method: Choose max, sum, probor, or Custom, for a custom
operation. This selection is not available for Sugeno-style fuzzy inference.
• Defuzzification method: For Mamdani-style inference, choose centroid,
bisector, mom (middle of maximum), som (smallest of maximum), lom
(largest of maximum), or Custom, for a custom operation. For Sugeno-style
inference, choose between wtaver (weighted average) or wtsum (weighted
sum).
See Also
mfedit, ruleedit, ruleview, surfview, anfisedit
3-31
gauss2mf
Purpose
3gauss2mf
Gaussian combination membership function.
Synopsis
y = gauss2mf(x,[sig1 c1 sig2 c2])
Description
The Gaussian function depends on two parameters sig and c as given by
–( x – c )
--------------------2 2σ
2
f ( x ;σ, c ) = e
The function gauss2mf is a combination of two of these. The first function,
specified by sig1 and c1, determines the shape of the leftmost curve. The second
function specified by sig2 and c2 determines the shape of the right-most curve.
Whenever c1 < c2, the gauss2mf function reaches a maximum value of 1.
Otherwise, the maximum value is less than one. The parameters are listed in
the order:
[sig1, c1, sig2, c2].
3-32
gauss2mf
Examples
See Also
x = (0:0.1:10)';
y1 = gauss2mf(x, [2 4 1 8]);
y2 = gauss2mf(x, [2 5 1 7]);
y3 = gauss2mf(x, [2 6 1 6]);
y4 = gauss2mf(x, [2 7 1 5]);
y5 = gauss2mf(x, [2 8 1 4]);
plot(x, [y1 y2 y3 y4 y5]);
set(gcf, 'name', 'gauss2mf', 'numbertitle', 'off');
dsigmf, gauss2mf, gbellmf, evalmf, mf2mf, pimf, psigmf, sigmf, smf, trapmf,
trimf, zmf
3-33
gaussmf
Purpose
3gaussmf
Gaussian curve built-in membership function.
Synopsis
y = gaussmf(x,[sig c])
Description
The symmetric Gaussian function depends on two parameters σ and c as given
by
–( x – c )
--------------------2 2σ
2
f ( x ;σ, c ) = e
The parameters for gaussmf represent the parameters σ and c listed in order
in the vector [sig c].
Example
x=0:0.1:10;
y=gaussmf(x,[2 5]);
plot(x,y)
xlabel('gaussmf, P=[2 5]')
1
0.75
0.5
0.25
0
0
See Also
3-34
2
4
6
gaussmf, P = [2 5]
8
10
dsigmf, gaussmf, gbellmf, evalmf, mf2mf, pimf, psigmf, sigmf, smf, trapmf,
trimf, zmf
gbellmf
Purpose
3gbellmf
Generalized bell-shaped built-in membership function.
Synopsis
y = gbellmf(x,params)
Description
The generalized bell function depends on three parameters a, b, and c as given
by
1
f ( x ;a, b, c ) = ----------------------------– c 2b
1+ x
----------a
where the parameter b is usually positive. The parameter c locates the center
of the curve. Enter the parameter vector params, the second argument for
gbellmf, as the vector whose entries are a, b, and c, respectively.
Example
x=0:0.1:10;
y=gbellmf(x,[2 4 6]);
plot(x,y)
xlabel('gbellmf, P=[2 4 6]')
1
0.75
0.5
0.25
0
0
See Also
2
4
6
gbellmf, P = [2 4 6]
8
10
dsigmf, gaussmf, gauss2mf, evalmf, mf2mf, pimf, psigmf, sigmf, smf, trapmf,
trimf, zmf
3-35
genfis1
Purpose
3genfis1
Generate an FIS structure from data without data clustering.
Synopsis
fismat = genfis1(data)
fismat = genfis1(data,numMFs,inmftype, outmftype)
Description
genfis1 generates a Sugeno-type FIS structure used as initial conditions
(initialization of the membership function parameters) for anfis training.
genfis1(data, numMFs, inmftype, outmftype) generates a FIS structure from a
training data set, data, using a grid partition on the data (no clustering).
The arguments for genfis1 are as follows:
• data is the training data matrix, which must be entered with all but the last
columns representing input data, and the last column representing the
single output.
• numMFs is a vector whose coordinates specify the number of membership
functions associated with each input. If you want the same number of
membership functions to be associated with each input, then it suffices to
make numMFs a single number.
• inmftype is a string array in which each row specifies the membership
function type associated with each input. Again, this can be a
one-dimensional single string if the type of membership functions associated
with each input is the same.
• outmftype is a string that specifies the membership function type associated
with the output. There can only be one output, since this is a Sugeno-type
system. The output membership function type must be either linear or
constant.
The number of membership functions associated with the output is the same
as the number of rules generated by genfis1. The default number of
membership functions, numMFs, is 2; the default input or output membership
function type is 'gbellmf'. These are used whenever genfis1 is invoked
without the last three arguments.
3-36
genfis1
Example
data = [rand(10,1) 10*rand(10,1)-5 rand(10,1)];
numMFs = [3 7];
mfType = str2mat('pimf','trimf');
fismat = genfis1(data,numMFs,mfType);
[x,mf] = plotmf(fismat,'input',1);
subplot(2,1,1), plot(x,mf);
xlabel('input 1 (pimf)');
[x,mf] = plotmf(fismat,'input',2);
subplot(2,1,2), plot(x,mf);
xlabel('input 2 (trimf)');
1
0.8
0.6
0.4
0.2
0
0
0.1
0.2
0.3
0.4
0.5
0.6
input 1 (pimf )
−4
−3
−2
−1
0.7
0.8
0.9
1
2
3
4
5
1
0.8
0.6
0.4
0.2
0
−5
See Also
0
1
input 2 (trimf)
anfis
3-37
genfis2
Purpose
Synopsis
Description
3genfis2
Generate an FIS structure from data using subtractive clustering.
fismat = genfis2(Xin,Xout,radii)
fismat = genfis2(Xin,Xout,radii,xBounds)
fismat = genfis2(Xin,Xout,radii,xBounds,options)
Given separate sets of input and output data, genfis2 generates an FIS using
fuzzy subtractive clustering. When there is only one output, genfis2 may be
used to generate an initial FIS for anfis training by first implementing
subtractive clustering on the data. genfis2 accomplishes this by extracting a
set of rules that models the data behavior. The rule extraction method first
uses the subclust function to determine the number of rules and antecedent
membership functions and then uses linear least squares estimation to
determine each rule’s consequent equations. This function returns an FIS
structure that contains a set of fuzzy rules to cover the feature space.
The arguments for genfis2 are as follows:
• Xin is a matrix in which each row contains the input values of a data point.
• Xout is a matrix in which each row contains the output values of a data point.
• radii is a vector that specifies a cluster center’s range of influence in each of
the data dimensions, assuming the data falls within a unit hyperbox. For
example, if the data dimension is 3 (e.g., Xin has two columns and Xout has
one column), radii = [0.5 0.4 0.3] specifies that the ranges of influence in the
first, second, and third data dimensions (i.e., the first column of Xin, the
second column of Xin, and the column of Xout) are 0.5, 0.4, and 0.3 times the
width of the data space, respectively. If radii is a scalar, then the scalar
value is applied to all data dimensions, i.e., each cluster center will have a
spherical neighborhood of influence with the given radius.
• xBounds is a 2-by-N optional matrix that specifies how to map the data in Xin
and Xout into a unit hyperbox, where N is the data (row) dimension. The first
row of xBounds contains the minimum axis range values and the second row
contains the maximum axis range values for scaling the data in each
dimension. For example, xBounds = [-10 0 -1; 10 50 1] specifies that data
values in the first data dimension are to be scaled from the range [-10 +10]
into values in the range [0 1]; data values in the second data dimension are
to be scaled from the range [0 50]; and data values in the third data
3-38
genfis2
dimension are to be scaled from the range [-1 +1]. If xBounds is an empty
matrix or not provided, then xBounds defaults to the minimum and
maximum data values found in each data dimension.
• options is an optional vector for specifying algorithm parameters to override
the default values. These parameters are explained in the help text for
subclust on page 3-72. Default values are in place when this argument is not
specified.
Examples
fismat = genfis2(Xin,Xout,0.5)
This is the minimum number of arguments needed to use this function. Here a
range of influence of 0.5 is specified for all data dimensions.
fismat = genfis2(Xin,Xout,[0.5 0.25 0.3])
This assumes the combined data dimension is 3. Suppose Xin has two columns
and Xout has one column, then 0.5 and 0.25 are the ranges of influence for each
of the Xin data dimensions, and 0.3 is the range of influence for the Xout data
dimension.
fismat = genfis2(Xin,Xout,0.5,[-10 -5 0; 10 5 20])
This specifies how to normalize the data in Xin and Xout into values in the
range [0 1] for processing. Suppose Xin has two columns and Xout has one
column, then the data in the first column of Xin are scaled from [-10 +10], the
data in the second column of Xin are scaled from [-5 +5], and the data in Xout
are scaled from [0 20].
See Also
subclust
3-39
gensurf
Purpose
3gensurf
Generate an FIS output surface.
Synopsis
gensurf(fis)
gensurf(fis,inputs,output)
gensurf(fis,inputs,output,grids,refinput)
Description
gensurf(fis) generates a plot of the output surface of a given fuzzy inference
system (fis) using the first two inputs and the first output.
gensurf(fis,inputs,output) generates a plot using the inputs (one or two)
and output (only one is allowed) given, respectively, by the vector, inputs, and
the scalar, output.
gensurf(fis,inputs,output,grids) allows you to specify the number of grids
in the X (first, horizontal) and Y (second, vertical) directions. If grids is a two
element vector, the grids in the X and Y directions can be set independently.
gensurf(fis,inputs,output,grids,refinput) can be used if there are more
than two outputs. The length of the vector refinput is the same as the number
of inputs.
• Enter NaNs for the entries of refinput corresponding to the inputs whose
surface is being displayed.
• Enter real double scalars to fix the values of other inputs.
[x,y,z]=gensurf(...) returns the variables that define the output surface
and suppresses automatic plotting.
3-40
gensurf
Example 1
a = readfis('tipper');
gensurf(a)
25
tip
20
15
10
5
10
8
10
6
8
6
4
4
2
food
Example 2
2
0
0
service
a = gensurf(Temp,[1 2],1,[20 20],[nan nan 0.2]);
generates the surface of a three-input FIS named Temp from its first two inputs
to its first output, while fixing a reference value for the third input at .2.
See Also
evalfis, surfview
3-41
getfis
Purpose
3getfis
Get fuzzy system properties.
Synopsis
getfis(a)
getfis(a,'fisprop')
getfis(a,'vartype',varindex,'varprop')
getfis(a,'vartype',varindex,'mf',mfindex)
getfis(a,'vartype',varindex,'mf',mfindex,'mfprop')
Description
This is the fundamental access function for the FIS structure. With this one
function you can learn about every part of the fuzzy inference system.
The arguments for getfis are as follows:
• a: the name of a workspace variable FIS structure.
• 'vartype': a string indicating the type of variable you want (input or
output).
• varindex: an integer indicating the index of the variable you want (1, for
input 1, for example).
• 'mf': a required string that indicates you are searching for membership
function information.
• mfindex: the index of the membership function for which you are seeking
information.
3-42
getfis
Examples
One input argument (output is the empty set)
a = readfis('tipper');
getfis(a)
Name = tipper
Type = mamdani
NumInputs = 2
InLabels =
service
food
NumOutputs = 1
OutLabels =
tip
NumRules = 3
AndMethod = min
OrMethod = max
ImpMethod = min
AggMethod = max
DefuzzMethod = centroid
Two input arguments
getfis(a,'type')
ans =
mamdani
Three input arguments (output is the empty set)
getfis(a,'input',1)
Name = service
NumMFs = 3
MFLabels =
poor
good
excellent
Range = [0 10]
Four input arguments
getfis(a,'input',1,'name')
ans =
service
3-43
getfis
Five input arguments
getfis(a,'input',1,'mf',2)
Name = good
Type = gaussmf
Params =
1.5000
5.0000
Six input arguments
getfis(a,'input',1,'mf',2,'name')
ans =
good
See Also
3-44
setfis, showfis
mam2sug
Purpose
3mam2sug
Transform Mamdani FIS into a Sugeno FIS.
Synopsis
sug_fis=mam2sug(mam_fis)
Description
mam2sug(mam_fis) transforms a (not necessarily single output) Mamdani FIS
structure mam_fis into a Sugeno FIS structure sug_fis. The returned Sugeno
system has constant output membership functions. These constants are
determined by the centroids of the consequent membership functions of the
original Mamdani system. The antecedent remains unchanged.
Examples
mam_fismat = readfis('mam22.fis');
sug_fismat = mam2sug(mam_fismat);
subplot(2,2,1); gensurf(mam_fismat,
title('Mamdani system (Output 1)');
subplot(2,2,2); gensurf(sug_fismat,
title('Sugeno system (Output 1)');
subplot(2,2,3); gensurf(mam_fismat,
title('Mamdani system (Output 2)');
subplot(2,2,4); gensurf(sug_fismat,
title('Sugeno system (Output 2)');
[1 2], 1);
[1 2], 1);
[1 2], 2);
[1 2], 2);
3-45
mf2mf
Purpose
3mf2mf
Translates parameters between membership functions.
Synopsis
outParams = mf2mf(inParams,inType,outType)
Description
This function translates any built-in membership function type into another,
in terms of its parameter set. In principle, mf2mf mimics the symmetry points
for both the new and old membership functions. Occasionally this translation
results in lost information, so that if the output parameters are translated back
into the original membership function type, the transformed membership
function will not look the same as it did originally.
The input arguments for mf2mf are as follows:
• inParams: the parameters of the membership function you are transforming
• inType: a string name for the type of membership function you are
transforming
• outType: a string name for the new membership function you are
transforming to
Examples
x=0:0.1:5;
mfp1 = [1 2 3];
mfp2 = mf2mf(mfp1,'gbellmf','trimf');
plot(x,gbellmf(x,mfp1),x,trimf(x,mfp2))
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
See Also
3-46
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
dsigmf, gaussmf, gauss2mf, gbellmf, evalmf, pimf, psigmf, sigmf, smf,
trapmf, trimf, zmf
mfedit
Purpose
Synopsis
3mfedit
Membership function editor.
mfedit('a')
mfedit(a)
mfedit
Description
mfedit('a') generates a membership function editor that allows you to
inspect and modify all the membership functions for your FIS stored in the file,
a.fis.
mfedit(a) operates on a MATLAB workspace variable, for a FIS structure, a.
mfedit alone brings up the membership function editor with no FIS loaded.
For each membership function you can change the name, the type, and the
parameters. Eleven built-in membership functions are provided for you to
choose from, although of course you can always create your own specialized
versions. Refer to “The Membership Function Editor” on page 2-52 for more
information about how to use mfedit.
3-47
mfedit
Select the icon for the variable on the upper left side of the diagram (under the
heading “FIS Variables”) to display its associated membership functions in the
plot region. Select membership functions by clicking once on them or their
labels.
Menu Items
On the Membership Function Editor, there is a menu bar that allows you to
open related GUI tools, open and save systems, and so on. The File menu for
the Membership Function Editor is the same as the one found on the FIS
Editor. Refer to fuzzy on page 3-29 for more information.
• Under Edit, select:
Add MF... to add membership functions to the current variable.
Add custom MF... to add a customized membership function to the current
variable.
Remove current MF to delete the current membership function.
Remove all MFs to delete all membership functions of the current variable.
Undo to undo the most recent change.
• Under View, select:
Edit FIS properties... to invoke the FIS Editor.
Edit rules... to invoke the Rule Editor.
View rules... to invoke the Rule Viewer.
View surface... to invoke the Surface Viewer.
Membership
Function
Pop-up Menu
There are 11 built-in membership functions to choose from, and you also have
the option of installing a customized membership function.
See Also
fuzzy, ruleedit, ruleview, surfview
3-48
newfis
Purpose
3newfis
Create new FIS.
Synopsis
a=newfis(fisName,fisType,andMethod,orMethod,impMethod, ...
aggMethod,defuzzMethod)
Description
This function creates new FIS structures. newfis has up to seven input
arguments, and the output argument is an FIS structure. The seven input
arguments are as follows:
• fisName is the string name of the FIS structure, fisName.fis you create.
• fisType is the type of FIS.
• andMethod, orMethod, impMethod, aggMethod, and defuzzMethod,
respectively provide the methods for AND, OR, implication, aggregation, and
defuzzification.
Examples
The following example shows what the defaults are for each of the methods:
a=newfis('newsys');
getfis(a)
returns
Name = newsys
Type = mamdani
NumInputs = 0
InLabels =
NumOutputs = 0
OutLabels =
NumRules
0
AndMethod
min
OrMethod
max
ImpMethod
min
AggMethod
max
DefuzzMethod
centroid
ans =
[newsys]
See Also
readfis, writefis
3-49
parsrule
Purpose
3parsrule
Parse fuzzy rules.
Synopsis
fis2 = parsrule(fis,txtRuleList)
fis2 = parsrule(fis,txtRuleList,ruleFormat)
fis2 = parsrule(fis,txtRuleList,ruleFormat,lang)
Description
This function parses the text that defines the rules (txtRuleList) for a
MATLAB workspace FIS variable, fis, and returns a FIS structure with the
appropriate rule list in place. If the original input FIS structure, fis, has any
rules initially, they are replaced in the new structure, fis2. Three different
rule formats (indicated by ruleFormat) are supported: 'verbose', “symbolic,”
and “indexed.” The default format is “verbose”. When the optional language
argument, lang, is used, the rules are parsed in verbose mode, assuming the
key words are in the language, lang. This language must be either 'english',
'francais', or 'deutsch'. The key language words in English are: if, then, is, AND,
OR, and NOT.
Examples
See Also
3-50
a = readfis('tipper');
ruleTxt = 'if service is poor then tip is generous';
a2 = parsrule(a,ruleTxt,'verbose');
showrule(a2)
ans =
1. If (service is poor) then (tip is generous) (1)
addrule, ruleedit, showrule
pimf
Purpose
Π -shaped built-in membership function.
3pimf
Synopsis
y = pimf(x,[a b c d])
Description
This spline-based curve is so named because of its Π -shape. This membership
function is evaluated at the points determined by the vector x. The parameters
a and d locate the “feet” of the curve, while b and c locate its “shoulders.”
Examples
x=0:0.1:10;
y=pimf(x,[1 4 5 10]);
plot(x,y)
xlabel('pimf, P=[1 4 5 10]')
1
0.75
0.5
0.25
0
0
See Also
2
4
6
pimf, P = [1 4 5 10]
8
10
dsigmf, gaussmf, gauss2mf, gbellmf, evalmf, mf2mf, psigmf, sigmf, smf,
trapmf, trimf, zmf
3-51
plotfis
Purpose
3plotfis
Plot an FIS.
Synopsis
plotfis(fismat)
Description
This function displays a high level diagram of an FIS, fismat. Inputs and their
membership functions appear to the left of the FIS structural characteristics,
while outputs and their membership functions appear on the right.
Examples
See Also
3-52
a = readfis('tipper');
plotfis(a)
evalmf, plotmf
plotmf
Purpose
3plotmf
Plot all of the membership functions for a given variable.
Synopsis
plotmf(fismat,varType,varIndex)
Description
This function plots all of the membership functions in the FIS called fismat
associated with a given variable whose type and index are respectively given
by varType ('input' or 'output'), and varIndex. This function can also be
used with the MATLAB function, subplot.
Examples
a = readfis('tipper');
plotmf(a,'input',1)
poor
good
excellent
1
Degree of belief
0.8
0.6
0.4
0.2
0
0
See Also
1
2
3
4
5
service
6
7
8
9
10
evalmf, plotfis
3-53
psigmf
Purpose
3psigmf
Built-in membership function composed of the product of two
sigmoidally-shaped membership functions.
Synopsis
y = psigmf(x,[a1 c1 a2 c2])
Description
The sigmoid curve plotted for the vector x depends on two parameters a and c
as given by
1
f ( x ;a, c ) = -----------------------------–a ( x – c )
1+e
psigmf is simply the product of two such curves plotted for the values of the
vector x
f1(x; a1, c1) * f2(x; a2, c2)
The parameters are listed in the order: [a1 c1 a2 c2].
Examples
x=0:0.1:10;
y=psigmf(x,[2 3 -5 8]);
plot(x,y)
xlabel('psigmf, P=[2 3 -5 8]')
1
0.75
0.5
0.25
0
0
See Also
3-54
2
4
6
psigmf, P = [2 3 −5 8]
8
10
dsigmf, gaussmf, gauss2mf, gbellmf, evalmf, mf2mf, pimf, sigmf, smf, trapmf,
trimf, zmf
readfis
Purpose
3readfis
Load an FIS from the disk.
Synopsis
fismat = readfis('filename')
Description
Read a fuzzy inference system from a .fis file (named filename) on the disk
and bring the resulting file into the workspace.
fismat = readfis (no input arguments) brings up a uigetfile dialog box to
assist with the name and directory location of the file.
Examples
fismat = readfis('tipper');
getfis(fismat)
returns
Name = tipper
Type = mamdani
NumInputs = 2
InLabels =
service
food
NumOutputs = 1
OutLabels =
tip
NumRules = 3
AndMethod = min
OrMethod = max
ImpMethod = min
AggMethod = max
DefuzzMethod = centroid
ans =
tipper
See Also
writefis
3-55
rmmf
Purpose
3rmmf
To remove membership function from an FIS.
Synopsis
fis = rmmf(fis,'varType',varIndex,'mf',mfIndex)
Description
fis = rmmf(fis,varType,varIndex,'mf',mfIndex) removes the membership
function, mfIndex, of variable type, varType, of index varIndex, from the fuzzy
inference system associated with the workspace FIS structure, fis:
• The string varType must be 'input' or 'output'.
• varIndex is an integer for the index of the variable. This index represents the
order in which the variables are listed.
• The argument 'mf' is a string representing the membership function.
• mfIndex is an integer for the index of the membership function. This index
represents the order in which the membership functions are listed.
Examples
a = newfis('mysys');
a = addvar(a,'input','temperature',[0 100]);
a = addmf(a,'input',1,'cold','trimf',[0 30 60]);
getfis(a,'input',1)
returns
Name = temperature
NumMFs = 1
MFLabels =
cold
Range = [0 100]
b = rmmf(a,'input',1,'mf',1);
getfis(b,'input',1)
returns
Name = temperature
NumMFs = 0
MFLabels =
Range = [0 100]
See Also
3-56
addmf, addrule, addvar, plotmf, rmvar
rmvar
Purpose
3rmvar
To remove variables from an FIS.
Synopsis
[fis2,errorStr] = rmvar(fis,'varType',varIndex)
fis2 = rmvar(fis,'varType',varIndex)
Description
fis2 = rmvar(fis,'varType',varIndex) removes the variable 'varType', of
index varIndex, from the fuzzy inference system associated with the
workspace FIS structure, fis:
• The string varType must be 'input' or 'output'.
• varIndex is an integer for the index of the variable. This index represents the
order in which the variables are listed.
[fis2,errorStr] = rmvar(fis,'varType',varIndex) returns any error
messages to the string, errorStr.
This command automatically alters the rule list to keep its size consistent with
the current number of variables. You must delete from the FIS any rule that
contains a variable you want to remove, before removing it. You cannot remove
a fuzzy variable currently in use in the rule list.
Examples
a = newfis('mysys');
a = addvar(a,'input','temperature',[0 100]);
getfis(a)
3-57
rmvar
returns
Name = mysys
Type
= mamdani
NumInputs = 1
InLabels =
temperature
NumOutputs = 0
OutLabels =
NumRules = 0
AndMethod = min
OrMethod = max
ImpMethod = min
AggMethod = max
DefuzzMethod = centroid
ans =
mysys
b = rmvar(a,'input',1);
getfis(b)
returns
Name = mysys
Type = mamdani
NumInputs = 0
InLabels =
NumOutputs = 0
OutLabels =
NumRules = 0
AndMethod = min
OrMethod = max
ImpMethod = min
AggMethod = max
DefuzzMethod = centroid
ans =
mysys
See Also
3-58
addmf, addrule, addvar, rmmf
ruleedit
Purpose
Synopsis
3ruleedit
Rule editor and parser.
ruleedit('a')
ruleedit(a)
Description
Language and Format
are options on this
GUI. Languages are
English, Deutsch, and
Francais. Formats are
verbose, symbolic,
and indexed.
The Rule Editor, when invoked using ruleedit('a'), is used to modify the
rules of an FIS structure stored in a file, a.fis. It can also be used to inspect
the rules being used by a fuzzy inference system.
To use this editor to create rules, you must first define all of the input and
output variables you want to use with the FIS editor. You can create the rules
using the listbox and check box choices for input and output variables,
connections, and weights. Refer to “The Rule Editor” on page 2-56 for more
information about how to use ruleedit.
The syntax ruleedit(a) is used when you want to operate on a workspace
variable for an FIS structure called a.
3-59
ruleedit
Menu Items
On the Rule Editor, there is a menu bar that allows you to open related GUI
tools, open and save systems, and so on. The File menu for the Rule Editor is
the same as the one found on the FIS Editor. Refer to fuzzy on page 3-29 for
more information.
• Use the following Edit menu item:
Undo to undo the most recent change.
• Use the following View menu items:
Edit FIS properties... to invoke the FIS Editor.
Edit membership functions... to invoke the Membership Function
Editor.
View rules... to invoke the Rule Viewer.
View surface... to invoke the Surface Viewer.
• Use the Options menu items:
Language to select the language: English, Deutsch, and Francais
Format to select the format:
` verbose uses the words “if,” “then,” “AND,” “OR,” and so on to create
actual sentences.
` symbolic substitutes some symbols for the words used in the verbose
mode. For example, “if A AND B then C” becomes “A & B => C.”
` indexed mirrors how the rule is stored in the FIS structure.
See Also
3-60
addrule, fuzzy, mfedit, parsrule, ruleview, showrule, surfview
ruleview
Purpose
Synopsis
3ruleview
Rule viewer and fuzzy inference diagram.
ruleview('a')
Description
The Rule Viewer invoked using ruleview('a') depicts the fuzzy inference
diagram for an FIS stored in a file, a.fis. It is used to view the entire
implication process from beginning to end. You can move around the line
indices that correspond to the inputs and then watch the system readjust and
compute the new output. Refer to “The Rule Viewer” on page 2-59 for more
information about how to use ruleview.
Menu Items
On the Rule Viewer, there is a menu bar that allows you to open related GUI
tools, open and save systems, and so on. The File menu for the Rule Viewer is
3-61
ruleview
the same as the one found on the FIS Editor. Refer to fuzzy on page 3-29 for
more information.
• Use the View menu items:
Edit FIS properties... to invoke the FIS Editor
Edit membership functions... to invoke the Membership Function Editor
Edit rules... to invoke the Rule Editor
View surface... to invoke the Surface Viewer
• Use the Options menu item:
Rule display format to set the format in which the rule appears. If you
click on the rule numbers on the left side of the fuzzy inference diagram,
the rule associated with that number will appear in the Status Bar at the
bottom of the Rule Viewer.
See Also
3-62
fuzzy, mfedit, ruleedit, surfview
setfis
3
3setfis
Purpose
Set fuzzy system properties.
Synopsis
a = setfis(a,'fispropname','newfisprop')
a = setfis(a,'vartype',varindex,'varpropname','newvarprop')
a = setfis(a,'vartype',varindex,'mf',mfindex, ...
'mfpropname','newmfprop');
Description
The command setfis can be called with three, five, or seven input arguments,
depending on whether you want to set a property of the entire FIS structure,
for a particular variable belonging to that FIS structure, or for a particular
membership function belonging to one of those variables. The arguments are:
• a — a variable name of an FIS from the workspace
• 'vartype' — a string indicating the variable type: input or output
• varindex — the index of the input or output variable
• 'mf' — a required string for the fourth argument of a 7-argument call for
setfis, indicating this variable is a membership function
• mfindex — the index of the membership function belonging to the chosen
variable
• 'fispropname' — a string indicating the property of the FIS field you want
to set: name, type, andmethod, ormethod, impmethod, aggmethod,
defuzzmethod
• 'newfisprop' — a string describing the name of the FIS property or method
you want to set
• 'varpropname' — a string indicating the name of the variable field you want
to set: name or range
• 'newvarprop' — a string describing the name of the variable you want to set
(for name), or an array describing the range of that variable (for range)
• 'mfpropname'— a string indicating the name of the membership function
field you want to set: name, type, or params.
• 'newmfprop' — a string describing the name or type of the membership
function field want to set (for name or type), or an array describing the range
of the parameters (for params)
3-63
setfis
Examples
Called with three arguments,
a = readfis('tipper');
a2 = setfis(a, 'name', 'eating');
getfis(a2, 'name');
Results in
out =
eating
If used with five arguments, setfis will update two variable properties.
a2 = setfis(a,'input',1,'name','help');
getfis(a2,'input',1,'name')
ans =
help
If used with seven arguments, setfis will update any of several membership
function properties.
a2 = setfis(a,'input',1,'mf',2,'name','wretched');
getfis(a2,'input',1,'mf',2,'name')
ans =
wretched
See Also
3-64
getfis
sffis
Purpose
3sffis
Fuzzy inference S-function for Simulink.
Synopsis
output = sffis(t,x,u,flag,fismat)
Description
This MEX-file is used by Simulink to undertake the calculation normally
performed by evalfis. It has been optimized to work in the Simulink
environment. This means, among other things, that sffis builds a data
structure in memory during the initialization phase of a Simulink simulation,
which it then continues to use until the simulation is complete.
The arguments t, x, and flag are standard Simulink S-function arguments
(see Chapter 8, “S-Functions” in the Using Simulink documentation). The
argument u is the input to the MATLAB workspace FIS structure, fismat. If,
for example, there are two inputs to fismat, then u will be a two-element
vector.
See Also
evalfis, fuzblock
3-65
showfis
Purpose
3showfis
Display annotated FIS.
Synopsis
showfis(fismat)
Description
showfis(fismat) prints a version of the MATLAB workspace variable FIS,
fismat, allowing you to see the significance and contents of each field of the
structure.
Examples
a = readfis('tipper');
showfis(a)
returns
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
3-66
Name
Type
Inputs/Outputs
NumInputMFs
NumOutputMFs
NumRules
AndMethod
OrMethod
ImpMethod
AggMethod
DefuzzMethod
InLabels
OutLabels
InRange
OutRange
InMFLabels
OutMFLabels
InMFTypes
tipper
mamdani
[2 1]
[3 2]
3
3
min
max
min
max
centroid
service
food
tip
[0 10]
[0 10]
[0 30]
poor
good
excellent
rancid
delicious
cheap
average
generous
gaussmf
gaussmf
showfis
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
42.
43.
44.
42.
43.
44.
42.
43.
44.
See Also
OutMFTypes
InMFParams
OutMFParams
Rule Antecedent
Rule Consequent
Rule Weigth
Rule Connection
gaussmf
trapmf
trapmf
trimf
trimf
trimf
[1.5 0 0 0]
[1.5 5 0 0]
[1.5 10 0 0]
[0 0 1 3]
[7 9 10 10]
[0 5 10 0]
[10 15 20 0]
[20 25 30 0]
[1 1]
[2 0]
[3 2]
1
2
3
1
1
1
2
1
2
getfis
3-67
showrule
Purpose
3showrule
Display FIS rules.
Synopsis
showrule(fis)
showrule(fis,indexList)
showrule(fis,indexList,format)
showrule(fis,indexList,format,Lang)
Description
This command is used to display the rules associated with a given system. It
can be invoked with one to four arguments. The first argument, fis, is
required. This is the MATLAB workspace variable name for a FIS structure.
The second (optional) argument indexList is the vector of rules you want to
display. The third argument (optional) is the string representing the format in
which the rules are returned. showrule can return the rule in any of three
different formats: 'verbose' (the default mode, for which English is the default
language), 'symbolic', and 'indexed', for membership function index
referencing.
When used with four arguments, the forth argument must be verbose, and
showrule(fis,indexList,format,lang) displays the rules in the language
given by lang, which must be either 'english', 'francais', or 'deutsch'.
3-68
showrule
Examples
a = readfis('tipper');
showrule(a,1)
ans =
1. If (service is poor) or (food is rancid) then (tip is cheap) (1)
showrule(a,2)
ans =
2. If (service is good) then (tip is average) (1)
showrule(a,[3 1],'symbolic')
ans =
3. (service==excellent) | (food==delicious) => (tip=generous) (1)
1. (service==poor) | (food==rancid) => (tip=cheap) (1)
showrule(a,1:3,'indexed')
ans =
1 1, 1 (1) : 2
2 0, 2 (1) : 1
3 2, 3 (1) : 2
See Also
parsrule, ruleedit, addrule
3-69
sigmf
Purpose
3sigmf
Sigmoidally-shaped built-in membership function.
Synopsis
y = sigmf(x,[a c])
Description
The sigmoidal function, sigmf(x,[a c]), as given below by f ( x, a, c ), is a
mapping on a vector x, and depends on two parameters a and c:
1
f ( x, a, c ) = -----------------------------–a ( x – c )
1+e
Depending on the sign of the parameter a, the sigmoidal membership function
is inherently open to the right or to the left, and thus is appropriate for
representing concepts such as “very large” or “very negative.” More
conventional-looking membership functions can be built by taking either the
product or difference of two different sigmoidal membership functions. You can
find out more about this in this chapter’s entries for dsigmf and psigmf.
Examples
x=0:0.1:10;
y=sigmf(x,[2 4]);
plot(x,y)
xlabel('sigmf, P=[2 4]')
1
0.75
0.5
0.25
0
0
See Also
3-70
2
4
6
sigmf, P = [2 4]
8
10
dsigmf, gaussmf, gauss2mf, gbellmf, evalmf, mf2mf, pimf, psigmf, smf,
trapmf, trimf, zmf
smf
Purpose
3smf
S–shaped built-in membership function.
Synopsis
y = smf(x,[a b])
Description
This spline-based curve is a mapping on the vector x, and is named because of
its S–shape. The parameters a and b locate the extremes of the sloped portion
of the curve.
Examples
x=0:0.1:10;
y=smf(x,[1 8]);
plot(x,y)
xlabel('smf, P=[1 8]')
1
0.75
0.5
0.25
0
0
See Also
2
4
6
smf, P = [1 8]
8
10
dsigmf, gaussmf, gauss2mf, gbellmf, evalmf, mf2mf, pimf, psigmf, sigmf,
trapmf, trimf, zmf
3-71
subclust
Purpose
3subclust
Find cluster centers with subtractive clustering.
Synopsis
[C,S] = subclust(X,radii,xBounds,options)
Description
This function estimates the cluster centers in a set of data by using the
subtractive clustering method. The subtractive clustering method assumes
each data point is a potential cluster center and calculates a measure of the
likelihood that each data point would define the cluster center, based on the
density of surrounding data points. The algorithm
• Selects the data point with the highest potential to be the first cluster center
• Removes all data points in the vicinity of the first cluster center (as
determined by radii), in order to determine the next data cluster and its
center location
• Iterates on this process until all of the data is within radii of a cluster center
The subtractive clustering method is an extension of the mountain clustering
method proposed by R. Yager.
The matrix X contains the data to be clustered; each row of X is a data point.
The variable radii is a vector of entries between 0 and 1 that specifies a cluster
center’s range of influence in each of the data dimensions, assuming the data
falls within a unit hyperbox. Small radii values generally result in finding a
few large clusters. Good values for radii are usually between 0.2 and 0.5.
For example, if the data dimension is two (X has two columns),
radii = [0.5 0.25] specifies that the range of influence in the first data
dimension is half the width of the data space and the range of influence in the
second data dimension is one quarter the width of the data space. If radii is a
scalar, then the scalar value is applied to all data dimensions, i.e., each cluster
center will have a spherical neighborhood of influence with the given radius.
xBounds is a 2-by-N matrix that specifies how to map the data in X into a unit
hyperbox, where N is the data dimension. This argument is optional if X is
already normalized. The first row contains the minimum axis range values and
the second row contains the maximum axis range values for scaling the data in
each dimension. For example, xBounds = [-10 -5; 10 5] specifies that data values
in the first data dimension are to be scaled from the range [-10 +10] into values
in the range [0 1]; data values in the second data dimension are to be scaled
from the range [-5 +5] into values in the range [0 1]. If xBounds is an empty
3-72
subclust
matrix or not provided, then xBounds defaults to the minimum and maximum
data values found in each data dimension.
The options vector can be used for specifying clustering algorithm parameters
to override the default values. These components of the vector options are
specified as follows:
• options(1) = quashFactor : This is the factor used to multiply the radii
values that determine the neighborhood of a cluster center, so as to quash
the potential for outlying points to be considered as part of that cluster.
(default: 1.25)
• options(2) = acceptRatio: This sets the potential, as a fraction of the
potential of the first cluster center, above which another data point will be
accepted as a cluster center. (default: 0.5)
• options(3) = rejectRatio : This sets the potential, as a fraction of the
potential of the first cluster center, below which a data point will be rejected
as a cluster center. (default: 0.15)
• options(4) = verbose: If this term is not zero, then progress information
will be printed as the clustering process proceeds. (default: 0)
The function returns the cluster centers in the matrix C; each row of C contains
the position of a cluster center. The returned S vector contains the sigma values
that specify the range of influence of a cluster center in each of the data
dimensions. All cluster centers share the same set of sigma values.
Examples
[C,S] = subclust(X,0.5)
This is the minimum number of arguments needed to use this function. A range
of influence of 0.5 has been specified for all data dimensions.
[C,S] = subclust(X,[0.5 0.25 0.3],[],[2.0 0.8 0.7])
This assumes the data dimension is 3 (X has 3 columns) and uses a range of
influence of 0.5, 0.25, and 0.3 for the first, second and third data dimension,
respectively. The scaling factors for mapping the data into a unit hyperbox will
be obtained from the minimum and maximum data values. The squashFactor
is set to 2.0, indicating that we only want to find clusters that are far from each
other. The acceptRatio is set to 0.8, indicating that we will only accept data
points that have a very strong potential for being cluster centers. The
3-73
subclust
rejectRatio is set to 0.7, indicating that we want to reject all data points
without a strong potential.
See Also
genfis2
References
Chiu, S., “Fuzzy Model Identification Based on Cluster Estimation,” Journal of
Intelligent & Fuzzy Systems, Vol. 2, No. 3, Sept. 1994.
Yager, R. and D. Filev, “Generation of Fuzzy Rules by Mountain Clustering,”
Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, pp. 209-219, 1994.
3-74
surfview
Purpose
Synopsis
3surfview
Output surface viewer.
surfview('a')
Description
The Surface Viewer invoked using surfview('a') is a GUI tool that lets you
examine the output surface of an FIS stored in a file, a.fis, for any one or two
inputs. Since it does not alter the fuzzy system or its associated FIS structure
in any way, it is a read-only editor. Using the pop-up menus, you select the two
input variables you want assigned to the two input axes (X and Y), as well the
output variable you want assigned to the output (or Z) axis. Select the
Evaluate button to perform the calculation and plot the output surface.
By clicking on the plot axes and dragging the mouse, you can manipulate the
surface so that you can view it from different angles.
If there are more than two inputs to your system, you must supply the constant
values associated with any unspecified inputs in the reference input section.
Refer to “The Surface Viewer” on page 2-61 for more information about how to
use surfview.
3-75
surfview
Menu Items
On the Surface Viewer, there is a menu bar that allows you to open related GUI
tools, open and save systems, and so on. The File menu for the Surface Viewer
is the same as the one found on the FIS Editor. Refer to fuzzy on page 3-29 for
more information.
• Use the View menu items:
Edit FIS properties... to invoke the FIS Editor.
Edit membership functions... to invoke the Membership Function Editor.
Edit rules... to invoke the Rule Editor.
View rules... to invoke the Rule Viewer.
• Use the Options menu items:
Plot to choose among eight different kinds of plot styles.
Color Map to choose among several different color schemes.
Always evaluate to automatically evaluate and plot a new surface every
time you make a change that affects the plot (like changing the number of
grid points). This is the default option. To deselect this option, select it once
more.
See Also
3-76
anfisedit, fuzzy, gensurf, mfedit, ruleedit, ruleview
trapmf
Purpose
3trapmf
Trapezoidal-shaped built-in membership function.
Synopsis
y = trapmf(x,[a b c d])
Description
The trapezoidal curve is a function of a vector, x, and depends on four scalar
parameters a, b, c, and d, as given by





f ( x ;a, b, c, d ) = 





0,
x≤a
x–a
------------, a ≤ x ≤ b
b–a
1, b ≤ x ≤ c
d–x
------------, c ≤ x ≤ d
d–c
0,
d≤x











or, more compactly, by
x–a
d–x
f ( x ;a, b, c, d ) = max min  ------------, 1, ------------ , 0

b – a
d – c 
The parameters a and d locate the “feet” of the trapezoid and the parameters b
and c locate the “shoulders.”
Examples
x=0:0.1:10;
y=trapmf(x,[1 5 7 8]);
plot(x,y)
xlabel('trapmf, P=[1 5 7 8]')
1
0.75
0.5
0.25
0
0
2
4
6
trapmf, P = [1 5 7 8]
8
10
3-77
trapmf
See Also
3-78
dsigmf, gaussmf, gauss2mf, gbellmf, evalmf, mf2mf, pimf, psigmf, sigmf, smf,
trimf, zmf
trimf
Purpose
3trimf
Triangular-shaped built-in membership function.
Synopsis
y = trimf(x,params)
y = trimf(x,[a b c])
Description
The triangular curve is a function of a vector, x, and depends on three scalar
parameters a, b, and c, as given by




f ( x ;a, b, c ) = 













0,
x≤a
x–a
------------, a ≤ x ≤ b
b–a
c–x
------------ , b ≤ x ≤ c
c–b
0,
c≤x
or, more compactly, by
x–a c–x
f ( x ;a, b, c ) = max min  ------------, ------------ , 0

b – a c – b 
The parameters a and c locate the “feet” of the triangle and the parameter c
locates the peak.
Examples
x=0:0.1:10;
y=trimf(x,[3 6 8]);
plot(x,y)
xlabel('trimf, P=[3 6 8]')
1
0.75
0.5
0.25
0
0
2
4
6
trimf, P = [3 6 8]
8
10
3-79
trimf
See Also
3-80
dsigmf, gaussmf, gauss2mf, gbellmf, evalmf, mf2mf, pimf, psigmf, sigmf, smf,
trapmf
writefis
Purpose
Synopsis
Description
3writefis
Save an FIS to the disk.
writefis(fismat)
writefis(fismat,'filename')
writefis(fismat,'filename','dialog'
writefis saves a MATLAB workspace FIS structure, fismat, as a .fis file on
disk.
writefis(fismat) brings up a dialog box to assist with the naming and
directory location of the file.
writefis(fismat,'filename') writes a .fis file corresponding to the FIS
structure, fismat, to a disk file called filename.fis. No dialog box is used and
the file is saved to the current directory.
writefis(fismat,'filename','dialog') brings up a dialog box with the
default name filename.fis supplied.
The extension .fis is only added to filename if it is not already included in the
name.
Examples
See Also
a = newfis('tipper');
a = addvar(a,'input','service',[0 10]);
a = addmf(a,'input',1,'poor','gaussmf',[1.5 0]);
a = addmf(a,'input',1,'good','gaussmf',[1.5 5]);
a = addmf(a,'input',1,'excellent','gaussmf',[1.5 10]);
writefis(a,'my_file')
readfis
3-81
zmf
Purpose
3zmf
Z-shaped built-in membership function.
Synopsis
y = zmf(x,[a b])
Description
This spline-based function of x is so named because of its Z-shape. The
parameters a and b locate the extremes of the sloped portion of the curve.
Examples
x=0:0.1:10;
y=zmf(x,[3 7]);
plot(x,y)
xlabel('zmf, P=[3 7]')
1
0.75
0.5
0.25
0
0
See Also
3-82
2
4
6
zmf, P = [3 7]
8
10
dsigmf, gaussmf, gauss2mf, gbellmf, evalmf, mf2mf, pimf, psigmf, sigmf, smf,
trapmf, trimf
Index
A
addmf 72, 76, 3-6
addrule 72, 76, 3-8
addvar 76, 3-9
dsigmf 27, 3-18
aggregation 36, 40, 44, 60, 63, 86, 132, 133
AND 30, 37, 63, 103
ANFIS 93
anfis 7, 92, 95, 104, 109, 112, 114, 125, 3-10
options 116
ANFIS Editor GUI 7, 45, 92, 95, 106, 109, 114,
3-14
anfisedit 67, 93, 95, 3-14
antecedent 35, 37, 59, 74, 86, 88, 132
E
B
backpropagation 104
C
chaotic time series 120
checking data 94, 118
checking error 119
clustering 120, 121, 128, 132
clustering algorithms 133
clustering GUI 128
consequent 32, 35, 40, 59, 74, 86, 132
antecedent 37
convertfis 3-16
D
defuzz 3-17
defuzzification 32, 36, 40, 49, 63, 95, 132
defuzzify 32, 41
degree of membership 20, 24, 33, 35, 38
distfcm 121
error tolerance 104
evalfis 73, 126, 3-19, 3-26
evalmf 3-21
F
fcm (fuzzy c-means) 120, 128, 3-22
findcluster 128, 3-24
FIS 43, 47, 49, 53, 58, 65, 67, 73, 84, 92, 95, 109,
125, 132, 3-10
C-code 130
Editor 45, 49, 68, 115
files 76
generating 100
Mamdani-type 49, 86, 91
matrix 73
saving a FIS 62
structure 93, 115, 117
Sugeno-type 86, 88, 95
Sugeno-type See also Sugeno-type inference
97
fuzblock 83, 3-27
fuzdemos 3-28
fuzzification 32, 37, 42, 49, 132
fuzzy 3-29
fuzzy clustering 115, 120
fuzzy c-means clustering 3-22
Fuzzy Inference System (FIS) 19, 45, 132
fuzzy operators 29, 32, 37, 39, 44, 58, 63, 71, 131
fuzzy set 20, 23, 26, 35, 38, 40, 60, 86, 88, 132
I-1
Index
G
hybrid method 104
membership function 23, 28, 35, 53, 55, 118, 133
mf editor 103
Membership Function Editor 45, 47, 52
membership functions
bell 27
custom 63
Gaussian 27
Pi 27
S 27
sigmoidal 27
Z 27
MF See also membership function
mf2mf 3-46
mfedit 3-47
min 43
model validation 94, 98
I
N
gauss2mf 27, 3-32
gaussian 27
gaussmf 27, 3-34
gbellmf 27, 3-35
genfis 111
genfis1 101, 3-36
genfis2 101, 126, 3-38
gensurf 70, 88, 90, 3-40
getfis 66, 76, 89, 3-42
glossary 132
grid partition 100
H
if-then rules 32
antecedent 32
consequent 32
implication 32, 35, 37, 39, 43, 60, 63, 86
implication See also if-then rules 32, 133
initfcm 121
neuro-fuzzy inference 93
newfis 72, 3-49
NOT 30, 103
O
OR 30, 37, 43, 103
L
logical operations 28
M
mam2sug 3-45
Mamdani’s method 36
Mamdani-style inference 133
Mamdani-type inference 35, 49, 86, 90
max 41, 43
I-2
P
parsrule 3-50
pimf 28, 3-51
plotfis 68, 3-52
plotmf 68, 90, 3-53
probabilistic OR 39
probor 41
psigmf 27, 3-54
Index
R
surfview 3-75
readfis 65, 73, 88, 3-55
rmmf 76, 3-56
rmvar 76, 3-57
T
Rule Editor 45, 56
rule formats 3-15, 3-60
Rule Viewer 45, 47, 59
ruleedit 3-59
ruleview 3-61
S
setfis 66, 76, 3-63
sffis 84, 3-65
showfis 67, 74, 76, 3-66
showrule 3-68
sigmf 27, 3-70
Simulink blocks
fuzzy controller with ruleviewer 81
Fuzzy Logic Controller 79, 83
Simulink, working with 78
singleton 36
sltank 79
smf 28, 3-71
stand-alone C-code 130
stand-alone fuzzy inference engine 130
step size 117
structure.field syntax 66, 73
subclust 3-72
subtractive clustering 100, 123, 128
Sugeno 123
Sugeno-type FIS See also Sugeno-type inference
101
Sugeno-type inference 37, 45, 50, 86, 88, 120, 124,
133
sum 41
Surface Viewer 45, 47, 61
T-conorm 31, 133
testing data 94, 97
T-norm 31, 133
training data 95, 99, 115
training error 117
trapezoidal 26
trapmf 26, 3-77
trimf 3-79
W
writefis 3-81
Z
zmf 28, 3-82
I-3