Design of fuzzy wavelet neural networks using the GA approach for function approximation and system identification

Abstract

In this paper, an efficient method is proposed to design fuzzy wavelet neural network (FWNN) for function learning and identification by tuning fuzzy membership functions and wavelet neural networks. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient genetic algorithm (GA) approach is used to adjust the parameters of dilation, translation, weights, and membership functions. By minimizing a quadratic measure of the error derived from the output of the system, the design problem can be characterized by the proposed GA formulation. Moreover, the solution is directly obtained without any need for complicated computations. The performance of our approximation is superior to that of existing methods. Several numerical design examples are likewise presented to demonstrate the design flexibility and usefulness of this presented approach.