Genetically optimized self-organizing fuzzy polynomial neural networks based on information granulation

Authors:
Hosung Park;Daehee Park;Sungkwun Oh
Affiliations:
School of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;School 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
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

In this study, we introduce and investigate a genetically optimized self-organizing fuzzy polynomial neural network with the aid of information granulation (IG_gSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. 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 GA-based design procedure being applied at each layer of IG_gSOFPNN leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network.