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A new class of wavelet networks for nonlinear system identification

Billings, S.A. and Wei, H.L. (2005) A new class of wavelet networks for nonlinear system identification. IEEE Transactions on Neural Networks, 16 (4). pp. 862-874. ISSN 1045-9227

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Abstract

A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions.

Item Type: Article
Copyright, Publisher and Additional Information: Copyright © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: nonlinear autoregressive with exogenous inputs (NARX) models, nonlinear system identification, orthogonal least squares, wavelet networks
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 02 Dec 2005
Last Modified: 04 Jun 2014 15:03
Published Version: http://dx.doi.org/10.1109/TNN.2005.849842
Status: Published
Refereed: Yes
Identification Number: 10.1109/TNN.2005.849842
URI: http://eprints.whiterose.ac.uk/id/eprint/787

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