A Concurrent Fuzzy Neural Network Approach for a Fuzzy Gaussian Neural Network

Abstract

Abstract. The aim of the paper is to introduce a concurrent fuzzy neural network approach, representing a winner-takes-all collection of fuzzy FUZZY GAUSSIAN NEURAL NETWORK (FGNN)The four-layer structure of the Fuzzy-Gaussian Neural Network (FGNN) is shown in [6]. It is a special type of neural network, by special type of FGNN understanding that it has so special the connections (between the second and third layers) and the operations with the nodes, too.It represents a modified version of Chen and Teng fuzzy neural network, by transforming the function of approximation into a function of classification. The change affects only the equations of the fourth layer, but the structure diagram is similar.The FGNN keeps the advantages of the original fuzzy net described by Chen and Teng [3] for identification in control systems:(a) its structure allows us to construct the fuzzy system rule by rule; (b) if the prior knowledge of an expert is available, then we can directly add some rule nodes and term nodes; (c) the number of rules do not increase exponentially with the number of inputs; (d) elimination of redundant nodes rule by rule. Its construction is based on fuzzy rules of the form: