General Parameter Radial Basis Function Neural Network Based Adaptive Fuzzy Systems

Abstract

In this paper an automatic fuzzy rule generation problem through the artificial neural network (ANN) approach is considered. The unknown fuzzy relation reconstruction problem is treated as an optimization of the structure and parameters of the neural network. The functional equivalence between some classes of fuzzy systems and radial basis function networks (RBFNs), namely, their localized sensitivity to input value, is a background of the proposed approach. The improved structure and advanced learning feature RBFN is developed based on General Parameter (GP) method of complex system identification. The criterion of the GP RBFN (General Parameter Radial Basis Function Network) structure optimality is derived using the GP steady state statistics. The derived criterion is used then for the development of the GP RBFN structure self-organization procedure. As a result, an Adaptive Fuzzy System (AFS) with capability to extract fuzzy If-Then rules from input and output sample data is proposed. Simulation examples are given.