Fuzzy Inference System Modeling

Build fuzzy inference systems and fuzzy trees

Fuzzy inference is the process of formulating input/output mappings using fuzzy logic. Fuzzy Logic Toolbox™ software provides command-line functions and an app for creating Mamdani and Sugeno fuzzy systems. For more information on fuzzy logic, see What Is Fuzzy Logic?. For more information on fuzzy inference, see Fuzzy Inference Process.

Apps

Fuzzy Logic DesignerDesign and test fuzzy inference systems

Functions

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mamfisMamdani fuzzy inference system
sugfisSugeno fuzzy inference system
genfisGenerate fuzzy inference system object from data
genfisOptionsOption set for genfis command
fisvarFuzzy variable
addInputAdd input variable to fuzzy inference system
addOutputAdd output variable to fuzzy inference system
removeInputRemove input variable from fuzzy inference system
removeOutputRemove output variable from fuzzy inference system
convertToSugenoTransform Mamdani fuzzy inference system into Sugeno fuzzy inference system
convertToStructConvert fuzzy inference system object into a structure
convertfisConvert previous versions of fuzzy inference data in current format
mfeditOpen Membership Function Editor
addMFAdd membership function to fuzzy variable
removeMFRemove membership function from fuzzy variable
fismfFuzzy membership function
ruleeditOpen Rule Editor
ruleviewOpen Rule Viewer
addRuleAdd rule to fuzzy inference system
showruleDisplay fuzzy inference system rules
fisruleFuzzy rule
updateUpdate fuzzy rule using fuzzy inference system
evalfisEvaluate fuzzy inference system
evalfisOptionsOption set for evalfis function
plotfisDisplay fuzzy inference system
plotmfPlot membership functions for input or output variable
surfviewOpen Surface Viewer
gensurfGenerate fuzzy inference system output surface
gensurfOptionsOption set for gensurf command
readfisLoad fuzzy inference system from file
writeFISSave fuzzy inference system to file
fistreeNetwork of connected fuzzy inference systems
evalmfEvaluate fuzzy membership function
gaussmfGaussian membership function
gbellmfGeneralized bell-shaped membership function
trimfTriangular membership function
dsigmfDifference between two sigmoidal membership functions
gauss2mfGaussian combination membership function
pimfPi-shaped membership function
psigmfProduct of two sigmoidal membership functions
sigmfSigmoidal membership function
smfS-shaped membership function
trapmfTrapezoidal membership function
zmfZ-shaped membership function
defuzzDefuzzify membership function
proborProbabilistic OR
fuzarithPerform fuzzy arithmetic

Topics

Fuzzy Logic Basics

What Is Fuzzy Logic?

Fuzzy logic uses linguistic variables, defined as fuzzy sets, to approximate human reasoning.

Foundations of Fuzzy Logic

A fuzzy logic system is a collection of fuzzy if-then rules that perform logical operations on fuzzy sets.

Fuzzy Inference

Fuzzy Inference Process

Fuzzy inference maps an input space to an output space using a series of fuzzy if-then rules.

What Is Mamdani-Type Fuzzy Inference?

In Mamdani systems, the output of each if-then rule is a fuzzy set. To determine a crisp output value, these fuzzy sets are aggregated and defuzzified.

What Is Sugeno-Type Fuzzy Inference?

In Sugeno systems, the output of each if-then rule is either constant or a linear function of the input variables. The final output value is the weighted average of all rule outputs.

Comparison of Sugeno and Mamdani Systems

Both Mamdani and Sugeno systems have several advantages depending on your specific application.

Build Fuzzy Inference Systems

Build Fuzzy Systems Using Fuzzy Logic Designer

Interactively construct a fuzzy inference system using the Fuzzy Logic Designer app.

Build Fuzzy Systems at the Command Line

Construct a fuzzy inference system at the MATLAB® command line.

Build Fuzzy Systems Using Custom Functions

You can replace the built-in membership functions and fuzzy inference functions with your own custom functions.

Fuzzy Trees

Fuzzy Trees

You can implement complex fuzzy inference systems as a collection of smaller interconnected fuzzy systems.

Featured Examples