Improvement of HSOFPNN Using Evolutionary Algorithm

Abstract

This paper presents genetically optimized Hybrid Self-Organizing Fuzzy Polynomial Neural Networks (gHSOFPNN). The architecture of the resulting gHSOFPNN results from a synergistic usage of the hybrid system generated by combining fuzzy polynomial neurons (FPNs)-based Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) with polynomial neurons (PNs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The augmented gHSOFPNN results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HSOFPNN. The GA-based design procedure being applied at each layer of gHSOFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HSOFPNN. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.