NDLI: A new class of wavelet networks for nonlinear system identification

Content Provider	IEEE Xplore Digital Library
Author	Billings, S.A. Hua-Liang Wei
Copyright Year	1990
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.
Sponsorship	IEEE Computational Intelligence Society
Page Count	13
File Size	734936
Starting Page	862
Ending Page	874
File Format	PDF
ISSN	10459227
Volume Number	16
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear systems Continuous wavelet transforms Discrete wavelet transforms Neural networks Least squares methods Signal processing algorithms Analysis of variance Modeling Artificial neural networks Feedforward neural networks wavelet networks (WNs) Nonlinear autoregressive with exogenous inputs (NARX) models nonlinear system identification orthogonal least squares (OLS)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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