genfis3

(To be removed) Generate fuzzy inference system from data using FCM clustering

genfis3 will be removed in a future release. Use genfis instead. For more information, see Version History.

Syntax

fis = genfis3(inputData,outputData)

fis = genfis3(inputData,outputData,type)

fis = genfis3(inputData,outputData,type,numClusters)

fis = genfis3(inputData,outputData,type,numClusters,options)

Description

fis = genfis3(inputData,outputData) creates a Sugeno FIS using fuzzy c-means (FCM) clustering by extracting a set of rules that models the training data behavior.

fis = genfis3(inputData,outputData,type) generates a FIS of the specified type, either Mamdani or Sugeno.

fis = genfis3(inputData,outputData,type,numClusters) specifies the number of clusters to use for the FCM clustering.

fis = genfis3(inputData,outputData,type,numClusters,options) specifies additional clustering options.

Examples

Generate Sugeno-Type FIS and Specify Number of Clusters

Create the input and output data.

Xin = [7*rand(50,1) 20*rand(50,1)-10];
Xout = 5*rand(50,1);

Generate a Sugeno-type FIS with 3 clusters.

opt = NaN(4,1);
opt(4) = 0;
fis = genfis3(Xin,Xout,'sugeno',3,opt);

The fourth input argument specifies the number of clusters. The fifth input argument, opt, specifies the options for the FCM algorithm. The NaN entries of opt specify default option values. Setting opt(4) to 0 turns off the display of iteration information at the command line.

To see the contents of fis, use showfis(fis).

Plot the input membership functions.

[x,mf] = plotmf(fis,'input',1);
subplot(2,1,1)
plot(x,mf)
xlabel('Membership Functions for Input 1')
[x,mf] = plotmf(fis,'input',2);
subplot(2,1,2)
plot(x,mf)
xlabel('Membership Functions for Input 2')

Input Arguments

inputData — Input data

array

Input data, specified as an N-column array, where N is the number of FIS inputs.

inputData and outputData must have the same number of rows.

outputData — Output data

array

Output data, specified as an M-column array, where M is the number of FIS outputs.

When using grid partitioning, outputData must have one column. If you specify more than one column for grid partitioning, genfis uses the first column as the output data.

inputData and outputData must have the same number of rows.

type — Fuzzy system type

'sugeno' (default) | 'mamdani'

Fuzzy system type, specified as either 'sugeno' or 'mamdani'.

numClusters — Number of FCM clusters

'auto' (default) | positive integer

Number of FCM clusters, specified as 'auto' or a positive integer.

The number of clusters determines the number of rules and membership functions in the generated FIS. cluster_n must be an integer or 'auto'. When cluster_n is 'auto', the function uses the subclust algorithm with a cluster range of influence of 0.5 and the minimum and maximum values of inputData and outputData as data scale factors to find the number of clusters. See subclust for more information.

options — Clustering options

vector

Clustering options, specified as a vector with the following elements:

Option	Description	Default
options(1)	Exponent for the fuzzy partition matrix U, specified as 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 a lot of overlap between potential clusters, then the calculated cluster centers might 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.	2.0
options(2)	Maximum number of iterations, specified as a positive integer.	100
options(3)	Minimum improvement in objective function between two consecutive iterations, specified as a positive scalar.	1e-5
options(4)	Information display flag indicating whether to display the objective function value after each iteration, specified as one of the following: true — Display objective function. false — Do not display objective function.	true

If any element of options is NaN, the default value for that option is used.

The clustering process stops when the maximum number of iterations is reached or when the objective function improvement between two consecutive iterations is less than the specified minimum.

Output Arguments

fis — Fuzzy system

sugfis object | mamfis object

Fuzzy system, returned as a FIS object.

The input membership function type is 'gaussmf'. By default, the output membership function type is 'linear'. However, if you specify type as 'mamdani', then the output membership function type is 'gaussmf'.

The following table summarizes the default inference methods.

Inference Method	Default
AND	prod
OR	probor
Implication	prod
Aggregation	sum
Defuzzification	wtaver

Version History

Introduced before R2006a

R2019b: Support for fuzzy inference system structures will be removed

Support for representing fuzzy inference systems as structures will be removed in a future release. Use mamfis and sugfis objects with this function instead. To convert existing fuzzy inference system structures to objects, use the convertfis function.

This change was announced in R2018b. Using fuzzy inference system structures with this function issues a warning starting in R2019b.

R2017a: To be removed

genfis3 will be removed in a future release. Use genfis instead. There are differences between these functions that require updates to your code.

Update Code

To generate a fuzzy system using grid partitioning, first create a default genfisOptions set.

opt = genfisOptions('FCMClustering');

You can modify the options using dot notation. Any options you do not modify remain at their default values.

Then, update your code to use genfis. For example, suppose your code has the following form.

fis = genfis3(inputData,outputData,type,cluster_n,fcmoptions);

Use the following code instead.

opt = genfisOptions('FCMClustering');
opt.FISType = type;
opt.NumClusters = cluster_n;
opt.Exponent = fcmoptions(1);
opt.MaxNumIteration = fcmoptions(2);
opt.MinImprovement = fcmoptions(3);
opt.Verbose = fcmoptions(4);
fis = genfis(inputData,outputData,opt);