23-06-2012, 03:52 PM
Fuzzy Logic Toolbox
[attachment=25292]
Description of Fuzzy Logic
In recent years, the number and variety of applications of fuzzy logic have
increased significantly. 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 why use of fuzzy logic has grown, you must first understand
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. However, in a wider
sense 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. Even in its more narrow definition, fuzzy logic differs
both in concept and substance from traditional multivalued logical systems.
In Fuzzy Logic Toolbox™ software, 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.
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. Fuzzy
logic is a more intuitive approach without the far-reaching complexity.
• Fuzzy logic is flexible.
With any given system, it is easy to layer on more functionality without
starting again from scratch.
• Fuzzy logic is tolerant of imprecise data.
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.
What Can Fuzzy Logic Toolbox™ Software Do?
You can create and edit fuzzy inference systems with Fuzzy Logic Toolbox
software. 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® software, you can easily test your fuzzy
system in a block diagram simulation environment.
Overview of Fuzzy Inference Process
This section describes the fuzzy inference process and uses the example of
the two-input, one-output, three-rule tipping problem “The Basic Tipping
Problem” on page 1-12 that you saw in the introduction in more detail. The
basic structure of this example is shown in the following diagram:
[attachment=25292]
Description of Fuzzy Logic
In recent years, the number and variety of applications of fuzzy logic have
increased significantly. 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 why use of fuzzy logic has grown, you must first understand
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. However, in a wider
sense 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. Even in its more narrow definition, fuzzy logic differs
both in concept and substance from traditional multivalued logical systems.
In Fuzzy Logic Toolbox™ software, 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.
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. Fuzzy
logic is a more intuitive approach without the far-reaching complexity.
• Fuzzy logic is flexible.
With any given system, it is easy to layer on more functionality without
starting again from scratch.
• Fuzzy logic is tolerant of imprecise data.
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.
What Can Fuzzy Logic Toolbox™ Software Do?
You can create and edit fuzzy inference systems with Fuzzy Logic Toolbox
software. 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® software, you can easily test your fuzzy
system in a block diagram simulation environment.
Overview of Fuzzy Inference Process
This section describes the fuzzy inference process and uses the example of
the two-input, one-output, three-rule tipping problem “The Basic Tipping
Problem” on page 1-12 that you saw in the introduction in more detail. The
basic structure of this example is shown in the following diagram: