Fuzzy Logic Toolbox 2.2.6

Category Intelligent Software>Fuzzy Logic Systems/Tools

Abstract The Fuzzy Logic Toolbox extends the MATLAB (see Note 1) technical computing environment with tools for the design of systems based on fuzzy logic. Graphical user interfaces (GUIs) guide you through the steps of fuzzy inference system design. Functions are provided for many common fuzzy logic methods, including fuzzy clustering and adaptive neurofuzzy learning.

The toolbox lets you model complex system behaviors using simple logic rules and then implements these rules in a fuzzy inference system. You can use the toolbox as a stand-alone fuzzy inference engine. Alternatively, you can use fuzzy inference blocks in Simulink (see Note 2) and simulate the fuzzy systems within a comprehensive model of the entire dynamic system.

Product provides GUIs to let you perform classical fuzzy system development and pattern recognition. Using the toolbox, you can -

1) Develop and analyze fuzzy inference systems;

2) Develop adaptive neurofuzzy inference systems;

3) Perform fuzzy clustering.

Building a Fuzzy Inference System - Fuzzy inference is a method that interprets the values in the input vector and, based on user-defined rules, assigns values to the output vector. Using the GUI editors and viewers in the Fuzzy Logic Toolbox, you can build the rules set, define the membership functions, and analyze the behavior of a fuzzy inference system (FIS).

The following editors and viewers are provided -

1) FIS Editor - Displays general information about a fuzzy inference system;

2) Membership Function Editor - Lets you display and edit the membership functions associated with the input and output variables of the FIS;

3) Rule Editor - Lets you view and edit fuzzy rules using one of three formats - full English-like syntax, concise symbolic notation, or an indexed notation;

4) Rule Viewer - Lets you view detailed behavior of a FIS to help diagnose the behavior of specific rules or study the effect of changing input variables;

5) Surface Viewer - Generates a 3-D surface from two input variables and the output of an FIS.

Modeling Using Fuzzy Logic - Product lets you apply neurofuzzy and clustering techniques to model and classify system behavior.

Adaptive Neurofuzzy Inference - Using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Editor, you can shape membership functions by training them with input/output data rather than specifying them manually. The toolbox uses a back propagation algorithm alone or in combination with a least squares method, enabling your fuzzy systems to learn from the data.

Fuzzy Clustering - Product provides support for fuzzy C-means and subtractive clustering, modeling techniques for data classification and modeling.

Simulating and Deploying Fuzzy Inference Systems - You can evaluate FIS performance by using the Fuzzy Logic Controller block in a Simulink model of your system. The Fuzzy Logic Controller block automatically generates a hierarchical block diagram representation for most fuzzy inference systems. This representation uses only built-in Simulink blocks, enabling efficient code generation (using Real-Time Workshop, available separately - see Note 3).

You can also save your FIS in ASCII format for use outside the MATLAB environment. The toolbox supplies a fuzzy inference engine that can execute your fuzzy system as a stand-alone application or embedded in an external application.

Note 1: MATLAB is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and FORTRAN.

Note 2: Simulink is an environment for multidomain simulation and Model-Based Design for dynamic and embedded systems.

Note 3: Real-Time Workshop generates and executes stand-alone C code for developing and testing algorithms modeled in Simulink and Embedded MATLAB code.

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Manufacturer Web Site The MathWorks, Inc.

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G6G Abstract Number 20028

G6G Manufacturer Number 102625