Genetically dynamic optimized self-organizing fuzzy polynomial neural networks with information granulation based FPNs

Authors:
Ho-Sung Park;Sung-Kwun Oh;Witold Pedrycz;Hyun-Ki Kim
Affiliations:
Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada;Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea
Venue:
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Year:
2005

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Abstract

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.