The hybrid multi-layer inference architecture and algorithm of FPNN based on FNN and PNN

Abstract

The study is concerned with an approach to the design of a new category of fuzzy neural networks. The proposed Fuzzy Polynomial Neural Networks (FPNN) with hybrid multi-layer inference architecture is based on fuzzy neural networks (FNN) and polynomial neural networks (PNN) for model identification of complex and nonlinear systems. The one and the other are considered as premise and consequence part of FPNN respectively. Therefore, the proposed FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN and PNN. We introduce two kinds of FPNN architectures, namely the basic and modified architectures depending on the connection points (nodes) of the layer of FNN. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process and to get output performance with superb predictive ability. The availability and feasibility of the FPNN is discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed FPNN can produce a model with higher accuracy and predictive ability than any other method presented previously.