Genetically Dynamic Optimized Self-organizing Fuzzy Polynomial Neural Networks with Information Granulation Based FPNs

摘要

In this study, we proposed genetically dynamic optimized self-organizing fuzzy polynomial neural network with information granulation based FPNs (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structurally and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we 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 performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.