The analysis and design of IG_gHSOFPNN by evolutionary optimization

Authors:
Ho-Sung Park;Tae-Chon Ahn
Affiliations:
School of Electrical Electronic and Information Engineering, Wonkwang University, Iksan, Chon-Buk, South Korea;School of Electrical Electronic and Information Engineering, Wonkwang University, Iksan, Chon-Buk, South Korea
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

In this paper, we introduce the analysis and design of Information granulation based genetically optimized Hybrid Self-Organizing Fuzzy Polynomial Neural Networks (IG_gHSOFPNN) by evolutionary optimization. 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 available within the HSOFPNN. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.