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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 Sugeno FIS, you can tune its membership function parameters you neuro-adaptive learning methods. This tuning method does not require Global Optimization Toolboxsoftware. For more information, see Neuro-Adaptive Learning and ANFIS.


Neuro-Fuzzy DesignerDesign, train, and test Sugeno-type fuzzy inference systems


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tunefisTune fuzzy inference system or tree of fuzzy inference systems
tunefisOptionsOption set for tunefis function
getTunableSettingsObtain tunable settings from fuzzy inference system
setTunableSet specified parameter settings as tunable or nontunable
getTunableValuesObtain values of tunable parameters from fuzzy inference system
setTunableValuesSpecify tunable parameter values of a fuzzy inference system
anfisTune Sugeno-type fuzzy inference system using training data
anfisOptionsOption set for anfis command


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RuleSettingsTunable parameter settings of fuzzy rules
VariableSettingsTunable parameter settings of fuzzy variables
MembershipFunctionSettingsTunable parameter settings of fuzzy membership functions
ClauseParametersParameter settings for rule clauses
NumericParametersTunable numeric parameter settings of membership functions


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.

Tune FIS Tree for Gas Mileage Prediction

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

Tune Fuzzy Systems 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.

Comparison of anfis and Neuro-Fuzzy Designer Functionality

You can design neuro-fuzzy systems either at the command line or using the Neuro-Fuzzy Designer app.

Train Adaptive Neuro-Fuzzy Inference Systems

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

Test Data Against Trained System

Validate trained neuro-fuzzy systems using checking data that is different from training data.

Save Training Error Data to MATLAB Workspace

When using Neuro-Fuzzy Designer, you can export your trained neuro-fuzzy model and training error data to the MATLAB® workspace for further analysis.

Predict Chaotic Time-Series using ANFIS

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

Modeling Inverse Kinematics in a Robotic Arm

Determine joint angles required to place the tip of a robotic arm in a desired location using a neuro-fuzzy model.

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.