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

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. © Springer-Verlag Berlin Heidelberg 2006.