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mamfis

Mamdani fuzzy inference system

Description

Use a mamfis object to represent a Mamdani fuzzy inference system (FIS). For more information on Mamdani systems, see What Is Mamdani-Type Fuzzy Inference?

As an alternative to Mamdani systems, you can create a Sugeno FIS using a sugfis object. For a comparison of Mamdani and Sugeno systems, see Comparison of Sugeno and Mamdani Systems.

Creation

To create a Mamdani FIS object, use one of the following methods:

  • The mamfis function.

  • If you have input and output training data (inputData and outputData, respectively), you can use the genfis function with the FCM clustering method.

    opt = genfisOptions('FCMClustering','FISType','mamdani');
    fis = genfis(inputData,outputData,opt);
  • If you have a .fis file for a Mamdani system, you can use the readfis function.

Syntax

fis = mamfis
fis = mamfis(Name,Value)

Description

example

fis = mamfis creates a Mamdani FIS with default property values. To modify the properties of the fuzzy system, use dot notation.

example

fis = mamfis(Name,Value) specifies FIS configuration information or sets object properties using name-value pair arguments. You can specify multiple name-value pairs. Enclose names in quotes.

Input Arguments

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Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'NumInputs',2 configures the fuzzy system to have two input variables

Number of FIS inputs, specified as the comma-separated pair consisting of 'NumInputs' and a nonnegative integer.

Number of membership functions for each FIS input, specified as the comma-separated pair consisting of 'NumInputMFs' and a positive integer.

Number of FIS outputs, specified as the comma-separated pair consisting of 'NumOutputs' and a nonnegative integer.

Number of membership functions for each FIS output, specified as the comma-separated pair consisting of 'NumOutputMFs' and a positive integer.

Membership function type for both input and output variables, specified as the comma-separated pair consisting of "MFType" and either "trimf" (triangular MF) or "gaussmf" (Gaussian MF). For each input and output variable, the membership functions are uniformly distributed over the variable range with approximately 80% overlap in the MF supports.

Flag for automatically adding rules, specified as the comma-separated pair consisting of "AddRules" and one of the following:

  • "allcombinations" — If both NumInputs and NumOutputs are greater than zero, create rules with antecedents that contain all input membership function combinations. Each rule consequent contains all the output variables and uses the first membership function of each output.

  • "none" — Create a FIS without any rules.

Properties

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FIS name, specified as a string or character vector.

AND operator method for combining fuzzified input values in a fuzzy rule antecedent, specified as one of the following:

  • "min" — Minimum of fuzzified input values

  • "prod" — Product of fuzzified input values

  • String or character vector — Name of a custom AND function in the current working folder or on the MATLAB® path

  • Function handle — Custom AND function in the current working folder or on the MATLAB path

For more information on using custom functions, see Build Fuzzy Systems Using Custom Functions.

For more information on fuzzy operators and the fuzzy inference process, see Fuzzy Inference Process.

OR operator method for combining fuzzified input values in a fuzzy rule antecedent, specified as one of the following:

  • "max" — Maximum of fuzzified input values.

  • "probor" — Probabilistic OR of fuzzified input values. For more information, see probor.

  • String or character vector — Name of a custom OR function in the current working folder or on the MATLAB path.

  • Function handle — Custom OR function in the current working folder or on the MATLAB path.

For more information on using custom functions, see Build Fuzzy Systems Using Custom Functions.

For more information on fuzzy operators and the fuzzy inference process, see Fuzzy Inference Process.

Implication method for computing the consequent fuzzy set, specified as one of the following:

  • "min" — Truncate the consequent membership function at the antecedent result value.

  • "prod" — Scale the consequent membership function by the antecedent result value.

  • String or character vector — Name of a custom implication function in the current working folder or on the MATLAB path.

  • Function handle — Custom implication function in the current working folder or on the MATLAB path.

For more information on using custom functions, see Build Fuzzy Systems Using Custom Functions.

For more information on implication and the fuzzy inference process, see Fuzzy Inference Process.

Aggregation method for combining rule consequents, specified as one of the following:

  • "max" — Maximum of consequent fuzzy sets

  • "sum" — Sum of consequent fuzzy sets

  • "probor" — Probabilistic OR of consequent fuzzy sets. For more information, see probor.

  • String or character vector — Name of a custom aggregation function in the current working folder or on the MATLAB path

  • Function handle — Custom aggregation function in the current working folder or on the MATLAB path

For more information on using custom functions, see Build Fuzzy Systems Using Custom Functions.

For more information on aggregation and the fuzzy inference process, see Fuzzy Inference Process.

Defuzzification method for computing crisp output values from the aggregated output fuzzy set, specified as one of the following:

  • "centroid" — Centroid of the area under the output fuzzy set

  • "bisector" — Bisector of the area under the output fuzzy set

  • "mom" — Mean of the values for which the output fuzzy set is maximum

  • "lom" — Largest value for which the output fuzzy set is maximum

  • "som" — Smallest value for which the output fuzzy set is maximum

  • String or character vector — Name of a custom defuzzification function in the current working folder or on the MATLAB path

  • Function handle — Custom defuzzification function in the current working folder or on the MATLAB path

For more information on using custom functions, see Build Fuzzy Systems Using Custom Functions.

For more information on defuzzification and the fuzzy inference process, see Fuzzy Inference Process.

FIS input variables, specified as a vector of fisvar objects. To add and remove input variables, use addInput and removeInput, respectively.

You can also create a vector of fisvar objects and assign it to Inputs using dot notation.

You can add membership functions to input variables using the addMF function.

FIS output variables, specified as a vector of fisvar objects. To add and remove output variables, use addOutput and removeOutput, respectively.

You can also create a vector of fisvar objects and assign it to Outputs using dot notation.

You can add membership functions to output variables using the addMF function.

FIS input variables, specified as a vector of fisrule objects. To add fuzzy rules, use the addRule function.

You can also create a vector of fisrule objects and assign it to Rules using dot notation.

To remove a rule, set the corresponding rule vector element to []. For example, to remove the tenth rule from the rule list, type:

fis.Rules(10) = [];

Flag for disabling consistency checks when property values change, specified as a logical value.

By default, when you change the value of a property of a mamfis object, the software verifies whether the new property value is consistent with the other object properties. These checks can affect performance, particularly when creating and updating fuzzy systems within loops.

To disable these checks, which results in faster FIS construction, set DisableSturcturalChecks to true.

Note

Disabling structural checks can result in an invalid mamfis object.

To reenable the consistency checks, first verify that the changes you made to the FIS are consistent and produce a valid mamfis object. Then, set DisableSturcturalChecks to false. If the mamfis object is invalid, reenabling the consistency checks generates an error.

Object Functions

addInputAdd input variable to fuzzy inference system
removeInputRemove input variable from fuzzy inference system
addOutputAdd output variable to fuzzy inference system
removeOutputRemove output variable from fuzzy inference system
addRuleAdd rule to fuzzy inference system
addMFAdd membership function to fuzzy variable
removeMFRemove membership function from fuzzy variable
evalfisEvaluate fuzzy inference system
writeFISSave fuzzy inference system to file

Examples

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Create a Mamdani fuzzy inference system with default property values.

fis = mamfis;

Modify the system properties using dot notation. For example, configure fis to use centroid defuzzification.

fis.DefuzzificationMethod = "centroid";

Alternatively, you can specify one of more FIS properties when you create a fuzzy system. For example, create a Mamdani fuzzy system with specified AND and OR methods.

fis = mamfis("AndMethod","prod","OrMethod","probor");

Create a Mamdani fuzzy inference system with three inputs and one output.

fis = mamfis("NumInputs",3,"NumOutputs",1);

Alternative Functionality

App

You can interactively create a Mamdani FIS using the Fuzzy Logic Designer app. You can then export the system to the MATLAB workspace.

Introduced in R2018b