Fuzzy Logic Toolbox
The Fuzzy Logic Toolbox provides apps to let you perform classical fuzzy system development and pattern recognition. Using the toolbox, you can:
In addition, the toolbox provides a fuzzy controller block that you can use in Simulink to model and simulate a fuzzy logic control system. From Simulink, you can generate C code for use in embedded applications that include fuzzy logic.
Like all MATLAB toolboxes, Fuzzy Logic Toolbox can be customized. You can inspect algorithms, modify source code, and add your own membership functions or defuzzification techniques.
Getting Started with Fuzzy Logic Toolbox (Part 1)
Use Fuzzy Logic Toolbox™ to design fuzzy logic systems.
Getting Started with Fuzzy Logic Toolbox (Part 2)
Define membership functions and rules for fuzzy inference systems.
Getting Started with Fuzzy Logic Toolbox (Part 3)
Simulate and analyze fuzzy inference systems.
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 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:
FIS Editor - Displays general information about a fuzzy inference system
Membership Function Editor - Lets you display and edit the membership functions associated with the input and output variables of the FIS
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
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
Surface Viewer - Generates a 3-D surface from two input variables and the output of an FIS
The Fuzzy Logic Toolbox lets you apply neurofuzzy and clustering techniques to model and classify system behavior.
Using the Neuro-Fuzzy Design app, 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.
The Fuzzy Logic Toolbox provides support for fuzzy C-means and subtractive clustering, modeling techniques for data classification and modeling.
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 Simulink Coder, available separately).
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.