Generating rules in FIS

As per my understanding, you want to create rules for fuzzy system automatically and want to reduce complexity involved in creating large number of rules.

Assuming that you have input-output data representing the system you want to model, MATLAB provides the "genfis" function to generate a Fuzzy Inference System (FIS) structure. This function creates rules based on all possible combinations of input membership functions. You can specify membership functions for each input using the options parameter in the "genfis" function. By default, it uses Grid Partitioning to generate the fuzzy system, but it also supports other clustering methods, such as the Subtractive Clustering method, which may help in reducing the number of rules.

You can refer to the documentation below for more information:

https://www.mathworks.com/help/fuzzy/genfis.html

Example of Grid Partitioning method:-

options = genfisOptions('GridPartition'); 
options.NumMembershipFunctions = [3 3]; % Example: 3 MFs for each of 2 inputs 
options.InputMembershipFunctionType = 'gaussmf'; % Example: Gaussian MFs 
 
% Generate initial FIS 
initialFIS = genfis(inputData, outputData, options); 

Alternatively, if you already have a collection of input-output data that you would like to use for modeling, you can also try using the "anfis" function. This function will automatically generate and optimize rules for a Fuzzy Inference System based on input-output training data using neuro-adaptive learning techniques.

For more information, please refer to the documentation below:

https://www.mathworks.com/help/fuzzy/neuro-adaptive-learning-and-anfis.html

Hope this helps