Recursive prediction error identification using the nonlinear Wiener model
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Applied Industrial Control--an Introduction
Applied Industrial Control--an Introduction
A comparison between HMLP and HRBF for attitude control
IEEE Transactions on Neural Networks
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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.