Simulation study of a kind of wavelet neural network algorithm used in approaching non-linear functions

A kind of wavelet neural network used in approaching non-linear functions is given. Geometrical structure of the network is analyzed and the method of parameter estimation of the network is given. Weights of the network are trained by the method of forgetting factor, and scale factor and displacement factor are studied by the predicting error method with an exellent recursive character excellent. Two kinds of methods in selecting the quantity of wavelet element were analyzed and given in detail. It is better in approaching the non-linear functions than the traditional BP neural network. The results of simulation indicate that the method is fast in its convergence speed and has a good approaching precision. It provides a new method in modelling non-linear systems besides offering a beneficial reference to the identification of complex nonlinear systems.