Wavelet Based Nonparametric NARX Models for Nonlinear Input-Output System Identification
Wei, H.L., Billings, S.A. and Balikhin, M.A.
(2003)
Wavelet Based Nonparametric NARX Models for Nonlinear Input-Output System Identification.
Research Report.
ACSE Research Report 831
.
Department of Automatic Control and Systems Engineering
Abstract
Wavelet based nonparametric additive NARX models are proposed for nonlinear input-output system identification. By expanding each functional component of the nonparametic NARX model into wavelet multiresolution expansions, the nonparametric estimation problem becomes a linear-in-the-parameters problem and least-squares-based methods such as the orthogonal forward regression (OFR) approach can be used to select the model terms and estimate the parameters. Wavelet based additive models, combined with model order determination and variable selection approaches, are capable of handling problems of high dimensionality.
Metadata
Item Type:
Monograph
Authors/Creators:
Wei, H.L.
Billings, S.A.
Balikhin, M.A.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords:
Nonlinear systems identification; Wavelets; NARX model; Nonparametric; Additive models.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)