Genetically dynamic optimization based fuzzy polynomial neural networks

Abstract

In this paper, we introduce a new architecture of genetically dynamic optimization based Fuzzy Polynomial Neural Networks (gdFPNN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed gdFPNN gives rise to a structurally and parametrically optimized network through an optimal parameters design available within FPN. Through the consecutive process of such structural and parametric optimization, an optimized and flexible gdFPNN is generated in a dynamic fashion. The performance of the proposed gdFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models. © Springer-Verlag Berlin Heidelberg 2005.