Fuzzy Inference System Tuning

Tune membership functions and rules of fuzzy systems

You can tune the membership function parameters and rules of your fuzzy inference system using Global Optimization Toolbox tuning methods such as genetic algorithms and particle swarm optimization. For more information, see Tuning Fuzzy Inference Systems.

If your system is a single-output type-1 Sugeno FIS, you can tune its membership function parameters using neuro-adaptive learning methods. This tuning method does not require Global Optimization Toolbox software. For more information, see Neuro-Adaptive Learning and ANFIS.

Apps

Neuro-Fuzzy Designer

Design, train, and test Sugeno-type fuzzy inference systems

Functions

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Tune Fuzzy Systems

`tunefis`	Tune fuzzy inference system or tree of fuzzy inference systems
`tunefisOptions`	Option set for `tunefis` function
`getTunableSettings`	Obtain tunable settings from fuzzy inference system
`setTunable`	Set specified parameter settings as tunable or nontunable
`getTunableValues`	Obtain values of tunable parameters from fuzzy inference system
`setTunableValues`	Specify tunable parameter values of a fuzzy inference system

Train ANFIS

`anfis`	Tune Sugeno-type fuzzy inference system using training data
`anfisOptions`	Option set for `anfis` command

Objects

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Tunable Settings

`RuleSettings`	Tunable parameter settings of fuzzy rules
`VariableSettings`	Tunable parameter settings of fuzzy variables
`MembershipFunctionSettings`	Tunable parameter settings for fuzzy membership functions
`MembershipFunctionSettingsType2`	Tunable parameter settings for type-2 fuzzy membership functions
`ClauseParameters`	Parameter settings for rule clauses
`NumericParameters`	Tunable numeric parameter settings of membership functions

Topics

Tune Fuzzy Systems

Tuning Fuzzy Inference Systems

Tune fuzzy membership function parameters and learn new fuzzy rules.

Tune Mamdani Fuzzy Inference System

Learn rules and tune membership function parameters for a Mamdani fuzzy system.

FIS Parameter Optimization with K-fold Cross Validation

To prevent overfitting during FIS parameter optimization, you can stop the tuning process early based on an unbiased evaluation of the model using validation data.

Tune FIS Tree for Gas Mileage Prediction

Tune the rules and membership function parameters for a tree of interconnected Sugeno fuzzy systems.

Predict Chaotic Time Series Using Type-2 FIS

Tune the rules and membership function parameters for a FIS with type-2 membership functions.

Tune Fuzzy Robot Obstacle Avoidance System Using Custom Cost Function

When you do not have training data, you can tune your fuzzy system using a custom cost function that simulates the FIS operation.

Train ANFIS Systems

Neuro-Adaptive Learning and ANFIS

You can tune Sugeno fuzzy inference systems using neuro-adaptive learning techniques similar to those used for training neural networks.

Train Adaptive Neuro-Fuzzy Inference Systems

Interactively create, train, and test neuro-fuzzy systems using the Neuro-Fuzzy Designer app.

Save Training Error Data to MATLAB Workspace

Predict Chaotic Time-Series using ANFIS

Train a neuro-fuzzy system for time-series prediction using the anfis command.

Adaptive Noise Cancellation Using ANFIS

Perform adaptive nonlinear noise cancellation using the anfis and genfis commands.

Gas Mileage Prediction

This example shows how to predict of fuel consumption (miles per gallon) for automobiles, using data from previously recorded observations.

Nonlinear System Identification

You can model nonlinear dynamic system behavior using adaptive neuro-fuzzy systems.

Featured Examples

Classify Pixels Using Fuzzy Systems

Tune separate fuzzy inference systems to classify pixels based on color and texture, and combine these systems into a fuzzy tree for image segmentation.

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