The Design of Genetically Optimized Self-Organizing Neural Networks with Polynomial and Fuzzy Polynomial Neurons

Abstract

In this study, we introduce and investigate a class of neural architectures of self-organizing neural networks (SONNs) that is based on a genetically optimized multilayer perceptron with polynomial neurons (PNs) or fuzzy polynomial neurons (FPNs), develop a comprehensive design methodology involving mechanisms of genetic optimization, and carry out a series of numeric experiments. We distinguish between two kinds of SONN architectures: (a) PN-based and (b) FPN-based SONNs. The augmented genetically optimized SONN (gSONN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one encountered in the conventional SONN. The genetic algorithm (GA)-based design procedure being applied at each layer of SONN leads to the selection of preferred nodes (PNs or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) available within the network.