Evolutionary Design of IG_gHSOFPNN with the aid of Information Granulation

Abstract

We introduce a new architecture of Information granulation based genetically optimized hybrid self-organizing fuzzy polynomial neural networks (IG_gHSOFPNN) that is based on a genetically optimized multi-layer perceptron and develop their comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The architecture of the resulting IG_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 IG_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 IG_gHSOFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HSOFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.