Least Squares Parameter Estimation Algorithms for Nonlinear Systems

Abstract

Least squares parameter estimation algorithms for nonlinear systems are investigated based on a nonlinear difference equation model. A modified extended least squares, an instrumental variable and a new suboptimal least squares algorithm are considered. The problems of input sensitivity, structure detection, model validation and input sensitivity, structure detection, model validation and input signal selection are also discussed in the nonlinear context.

Metadata

Item Type:	Monograph
Authors/Creators:	Billings, S.A. Voon, W.S.F.
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.
Dates:	Published: April 1983
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports
Depositing User:	MRS ALISON THERESA BARNETT
Date Deposited:	11 Sep 2013 08:51
Last Modified:	25 Oct 2016 00:06
Status:	Published
Publisher:	Department of Control Engineering, University of Sheffield, Mappin Street, Sheffield
Series Name:	ACSE Report 225
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:76436

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Least Squares Parameter Estimation Algorithms for Nonlinear Systems

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