genfis1

(To be removed) Generate Fuzzy Inference System structure from data using grid partition

genfis1 will be removed in a future release. Use genfis instead. For more information, see Compatibility Considerations.

Syntax

fismat = genfis1(data) 
fismat = genfis1(data,numMFs,inmftype,outmftype) 

Description

genfis1 generates a Sugeno-type FIS structure used as initial conditions (initialization of the membership function parameters) for anfis training.

genfis1(data) generates a single-output Sugeno-type fuzzy inference system using a grid partition on the data.

genfis1(data,numMFs,inmftype,outmftype) generates an FIS structure from a training data set, data, with the number and type of input membership functions and the type of output membership functions explicitly specified.

The arguments for genfis1 are as follows:

• data is the training data matrix, which must be entered with all but the last columns representing input data, and the last column representing the single output.

• numMFs is a vector whose coordinates specify the number of membership functions associated with each input. If you want the same number of membership functions to be associated with each input, then specify numMFs as a single number.

• inmftype is a character array in which each row specifies the membership function type associated with each input. This can be a character vector if the type of membership functions associated with each input is the same.

• outmftype is a character vector that specifies the membership function type associated with the output. There can only be one output, because this is a Sugeno-type system. The output membership function type must be either linear or constant. The number of membership functions associated with the output is the same as the number of rules generated by genfis1.

The default number of membership functions, numMFs, is 2; the default input membership function type is 'gbellmf'; and the default output membership function type is 'linear'. These are used whenever genfis1 is invoked without the last three arguments.

The following table summarizes the default inference methods.

Inference Method	Default
AND	prod
OR	max
Implication	prod
Aggregation	max
Defuzzification	wtaver

Examples

Generate FIS Using Grid Partitioning

Generate a FIS using grid partitioning.

data = [rand(10,1) 10*rand(10,1)-5 rand(10,1)];
numMFs = [3 7];
mfType = char('pimf','trimf');
fismat = genfis1(data,numMFs,mfType);

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

Plot the FIS input membership functions.

[x,mf] = plotmf(fismat,'input',1);
subplot(2,1,1), plot(x,mf)
xlabel('input 1 (pimf)')
[x,mf] = plotmf(fismat,'input',2);
subplot(2,1,2), plot(x,mf)
xlabel('input 2 (trimf)')

Compatibility Considerations

genfis1 will be removed

Not recommended starting in R2017a

genfis1 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('GridPartition');

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, if your code has the following form:

fis = genfis1(data,numMFs,inmftype,outmftype);

Use the following code instead:

opt = genfisOptions('GridPartition');
opt.NumMembershipFunctions = numMFs;
opt.InputMembershipFunctionType = inmftype;
opt.OutputMembershipFunctionType = outmftype;
inputData = data(:,end-1);
outputData = data(:,end);
fis = genfis(inputData,outputData,opt);