International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Genetically Optimized Fuzzy Polynomial Neural Network and Its Application to Multi-variable Software Process

  • Lee In-Tae (Department of Electrical Engineering, The University of Suwon) ;
  • Oh Sung-Kwun (Department of Electrical Engineering, The University of Suwon) ;
  • Kim Hyun-Ki (Department of Electrical Engineering, The University of Suwon) ;
  • Pedrycz Witold (Department of Electrical and Computer Engineering, University of Alberta)
  • Published : 2006.03.01
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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 FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. 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 advanced 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.

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