FSPN-Based genetically optimized fuzzy polynomial neural networks

Authors:
Sung-Kwun Oh;Seok-Beom Roh;Daehee Park;Yong-Kab Kim
Affiliations:
Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2005

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Abstract

In this paper, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy set-based polynomial neurons (FSPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.