Optimization of self-organizing fuzzy polynomial neural networks with the aid of granular computing and evolutionary algorithm

Authors:
Ho-Sung Park;Sung-Kwun Oh;Tae-Chon Ahn
Affiliations:
School of Electrical Electronic and Information Engineering, Wonkwang University, Iksan, Chon-Buk, South Korea;Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;School of Electrical Electronic and Information Engineering, Wonkwang University, Iksan, Chon-Buk, South Korea
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

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Abstract

In this study, we introduce and investigate a class of intelligence architectures of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized Fuzzy Polynomial Neurons(FPNs), develop a comprehensive design methodology involving mechanisms of genetic algorithms and information granulation. 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 SOFPNN 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.