Orthogonal wavelet neural networks applying to identification of Wiener model

In this paper, an orthogonal wavelet-based neural network (OWNN) is propose d, In the proposed OWNN both the orthogonal scaling functions and the corre sponding mother wavelets are combined as the nonlinear activation function. The OWNN is applied to identify a Wiener-type cascade dynamical model. A l inear autoregressive moving average (ARMA) model is used as the dynamic sub systems and the OWNN is employed as the nonlinear static subsystem. A Wiene r model identification algorithm is formed by combining the proposed OWNN w ith the conventional least squares method.