Design of Fuzzy Relation-Based Polynomial Neural Networks Using Information Granulation and Symbolic Gene Type Genetic Algorithms

摘要

In this study, we introduce and investigate a genetically optimized fuzzy relation-based polynomial neural networks with the aid of information granulation (IG_gFRPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization with symbolic gene type. With the aid of the information granules based on C-Means clustering, we can determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of IG_gFRPNN leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. The proposed model is contrasted with the performance of the conventional intelligent models shown in the literatures.