Automatic Generation of Distributed Simplification Fuzzy Rules by Fuzzy Neural Network

Abstract

This paper proposes an automatic generation technique for distributed simplification fuzzy rules in order to make fuzzy If-Then rules having high general-purpose capability and simple algorithm. The rules are generated by multilayer fuzzy neural network paying attention to fuzzy partitions. This technique improves the fault of conventional methods, which have a constraint in the calculation of membership functions in the antecedent part because the automatic tuning is carried out only to the consequent part parameters in the distributed simplification fuzzy rules. The technique is also simpler than the conventional methods. Results of identification of some nonlinear functions were shown and compared with those by conventional methods. Finally, it was verified that this technique had higher general-purpose capability and higher identification accuracy than those of the conventional methods.