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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 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.


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 function


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RuleSettingsTunable parameter settings of fuzzy rules
VariableSettingsTunable parameter settings of fuzzy variables
MembershipFunctionSettingsTunable parameter settings for fuzzy membership functions
MembershipFunctionSettingsType2Tunable parameter settings for type-2 fuzzy membership functions
ClauseParametersParameter settings for rule clauses
NumericParametersTunable numeric parameter settings of membership functions


Tune Fuzzy Systems

Train ANFIS Systems