Title :
Orthogonal wavelet neural networks applying to identification of Wiener model
Author :
Fang, Yong ; Chow, Tommy W S
Author_Institution :
Dept. of Electron. Eng., Hong Kong Polytech., Kowloon, Hong Kong
fDate :
4/1/2000 12:00:00 AM
Abstract :
In this paper, an orthogonal wavelet-based neural network (OWNN) is proposed. In the proposed OWNN both the orthogonal scaling functions and the corresponding mother wavelets are combined as the nonlinear activation function. The OWNN is applied to identify a Wiener-type cascade dynamical model. A linear autoregressive moving average (ARMA) model is used as the dynamic subsystems and the OWNN is employed as the nonlinear static subsystem. A Wiener model identification algorithm is formed by combining the proposed OWNN with the conventional least squares method
Keywords :
autoregressive moving average processes; identification; neural nets; transfer functions; wavelet transforms; Wiener model identification algorithm; Wiener-type cascade dynamical model; autoregressive moving average model; dynamic subsystems; least squares method; linear ARMA model; nonlinear activation function; nonlinear static subsystem; orthogonal scaling functions; orthogonal wavelet neural networks; Autoregressive processes; Control system synthesis; Function approximation; Learning systems; Least squares methods; Multilayer perceptrons; Multiresolution analysis; Neural networks; Power system modeling; Wavelet analysis;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
