Identification of Wiener model using radial basis functions neural networks

Abstract

A new method is introduced for the identification of Wiener model. The Wiener model consists of a linear dynamic block followed by a static nonlinearity. The nonlinearity and the linear dynamic part in the model are identified by using radial basis functions neural network (RBFNN) and autoregressive moving average (ARMA) model, respectively. The new algorithm makes use of the well known mapping ability of RBFNN. The learning algorithm based on least mean squares (LMS) principle is derived for the training of the identification scheme. The proposed algorithm estimates the weights of the RBFNN and the coefficients of ARMA model simultaneously. © Springer-Verlag Berlin Heidelberg 2002.