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genfisOptions

Option set for genfis command

Syntax

opt = genfisOptions(clusteringType)
opt = genfisOptions(clusteringType,Name,Value)

Description

example

opt = genfisOptions(clusteringType) creates a default option set for generating a fuzzy inference system structure using genfis. The option set, opt, contains different options that depend on the specified clustering algorithm, clusteringType. Use dot notation to modify this option set for your specific application. Options that you do not modify retain their default values.

example

opt = genfisOptions(clusteringType,Name,Value) creates an option set with options specified by one or more Name,Value pair arguments.

Examples

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Create a default option set for the grid partitioning generation method.

opt = genfisOptions('GridPartition');

Modify the options using dot notation. For example, specify 3 membership functions for the first input and 4 membership functions for the second input.

opt.NumMembershipFunctions = [3 4];

You can also specify options when creating the option set. For example, create an option set for FCM clustering using 4 clusters.

opt2 = genfisOptions('FCMClustering','NumClusters',4);

Input Arguments

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Clustering method for defining membership functions and fuzzy rules, specified as one of the following:

  • 'GridPartition' — Generate input membership functions by uniformly partitioning the input variable ranges, and create a single-output Sugeno fuzzy system. The fuzzy rule base contains one rule for each input membership function combination.

  • 'SubtractiveClustering' — Generate a Sugeno fuzzy system using membership functions and rules derived from data clusters found using subtractive clustering of input and output data. For more information on subtractive clustering, see subclust.

  • 'FCMClustering' — Generate a fuzzy system using membership function and rules derived from data clusters found using FCM clustering of input and output data. For more information on FCM clustering, see fcm.

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: 'InputMembershipFunctionType','trimf' sets triangular input membership functions for the grid partitioning algorithm.

Grid Partitioning Options

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Number of input membership functions for each input variable, specified as the comma-separated pair consisting of 'NumMembershipFunctions' and one of the following:

  • Integer greater than 1 — Specify the same number of membership functions for all inputs.

  • Vector of integer greater than 1 with length equal to the number of inputs — Specify a different number of membership functions for each input.

Input membership function type, specified as the comma-separated pair consisting of 'InputMembershipFunctionType' and one of the following:

  • Character vector or string — Specify one of the following membership function types for all inputs.

    Membership function typeDescriptionFor more information
    'gbellmf'Generalized bell-shaped membership functiongbellmf
    'gaussmf'Gaussian membership functiongaussmf
    'gauss2mf'Gaussian combination membership functiongauss2mf
    'trimf'Triangular membership functiontrimf
    'trapmf'Trapezoidal membership functiontrapmf
    'sigmf'Sigmoidal membership functionsigmf
    'dsigmf'Difference between two sigmoidal membership functionsdsigmf
    'psigmf'Product of two sigmoidal membership functionspsigmf
    'zmf'Z-shaped membership functionzmf
    'pimf'Pi-shaped membership functionpimf
    'smf'S-shaped membership functionsmf
    Character vector or stringName of a custom membership function in the current working folder or on the MATLAB® pathBuild Fuzzy Systems Using Custom Functions
  • Character array or string array — Specify a different membership function type for each input. For example, specify different membership functions for a three-input system:

    ["gbellmf","gaussmf","trimf"]

Output membership function type for a single-output Sugeno system, specified as the comma-separated pair consisting of 'OutputMembershipFunctionType' and one of the following:

  • 'linear' — The output of each rule is a linear function of the input variables, scaled by the antecedent result value.

  • 'constant' — The output of each rule is a constant, scaled by the antecedent result value.

Subtractive Clustering Options

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Range of influence of the cluster center for each input and output assuming the data falls within a unit hyperbox, specified as the comma-separated pair consisting of 'ClusterInfluenceRange' one of the following:

  • Scalar value in the range [0 1] — Use the same influence range for all inputs and outputs.

  • Vector — Use different influence ranges for each input and output.

Specifying a smaller range of influence usually creates more and smaller data clusters, producing more fuzzy rules.

Data scale factors for normalizing input and output data into a unit hyperbox, specified as the comma-separated pair consisting of 'DataScale' and a 2-by-N array, where N is the total number of inputs and outputs. Each column of DataScale specifies the minimum value in the first row and the maximum value in the second row for the corresponding input or output data set.

When DataScale is 'auto', the genfis command uses the actual minimum and maximum values in the data to be clustered.

Squash factor for scaling the range of influence of cluster centers, specified as the comma-separated pair consisting of 'SquashFactor' and a positive scalar. A smaller squash factor reduces the potential for outlying points to be considered as part of a cluster, which usually creates more and smaller data clusters.

Acceptance ratio, defined as a fraction of the potential of the first cluster center, above which another data point is accepted as a cluster center, specified as the comma-separated pair consisting of 'AcceptRatio' and a scalar value in the range [0, 1]. The acceptance ratio must be greater than the rejection ratio.

Rejection ratio, defined as a fraction of the potential of the first cluster center, below which another data point is rejected as a cluster center, specified as the comma-separated pair consisting of 'RejectRatio' and a scalar value in the range [0, 1]. The rejection ratio must be less than acceptance ratio.

Information display flag indicating whether to display progress information during clustering, specified as the comma-separated pair consisting of 'Verbose' and one of the following:

  • false — Do not display progress information.

  • true — Display progress information.

Custom cluster centers, specified the comma-separated pair consisting of 'CustomClusterCenters' and as a C-by-N array, where C is the number of clusters and N is the total number of inputs and outputs.

FCM Clustering Options

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Fuzzy inference system type, specified as the comma-separated pair consisting of 'FISType' and one of the following:

  • 'sugeno' — Sugeno-type fuzzy system

  • 'mamdani' — Mamdani-type fuzzy system

For more information on the types of fuzzy inference systems, see Types of Fuzzy Inference Systems.

Number of clusters to create, specified as the comma-separated pair consisting of 'NumClusters' and 'auto' or an integer greater than 1. When NumClusters is 'auto', the genfis command estimates the number of clusters using subtractive clustering with a cluster influence range of 0.5.

NumClusters determines the number of rules and membership functions in the generated FIS.

Exponent for the fuzzy partition matrix, specified as the comma-separated pair consisting of 'Exponent' and a scalar greater than 1.0. This option controls the amount of fuzzy overlap between clusters, with larger values indicating a greater degree of overlap.

If your data set is wide with significant overlap between potential clusters, then the calculated cluster centers can be very close to each other. In this case, each data point has approximately the same degree of membership in all clusters. To improve your clustering results, decrease this value, which limits the amount of fuzzy overlap during clustering.

For an example of fuzzy overlap adjustment, see Adjust Fuzzy Overlap in Fuzzy C-Means Clustering.

Maximum number of iterations, specified as the comma-separated pair consisting of 'MaxNumIteration' and a positive integer.

Minimum improvement in objective function between two consecutive iterations, specified as the comma-separated pair consisting of 'MinImprovement' and a positive scalar.

Information display flag indicating whether to display the objective function value after each iteration, specified as the comma-separated pair consisting of 'Verbose' and one of the following:

  • true — Display objective function.

  • false — Do not display objective function.

Output Arguments

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Option set for genfis command, returned as a genfisOptions option set. The options in the option set depend on the specified clusteringType.

See Also

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Introduced in R2017a