Design of fuzzy polynomial neural networks with the aid of genetic fuzzy granulation and its application to multi-variable process system

Authors:
Sung-Kwun Oh;In-Tae Lee;Jeoung-Nae Choi
Affiliations:
Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks (FPNN) by means of genetically optimized Fuzzy Polynomial Neuron (FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms (GAs). The conventional FPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed genetic algorithms-based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System (MIS) dataset to evaluate the performance of the proposed model.