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

Volume

47

Issue

4

fYear

2000

fDate

4/1/2000 12:00:00 AM

Firstpage

591

Lastpage

593

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;

fLanguage

English

Journal_Title

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on

Publisher

ieee

ISSN

1057-7122

Type

jour

DOI

10.1109/81.841863

Filename

841863