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Conference Paper: Approximate QR-based algorithms for recursive nonlinear least squares estiamtion

TitleApproximate QR-based algorithms for recursive nonlinear least squares estiamtion
Authors
KeywordsElectronics
Issue Date2005
PublisherIEEE.
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2005, p. 4333-4336 How to Cite?
AbstractThis paper proposes new approximate QR-based algorithms for recursive nonlinear least squares (NLS) estimation. Two QR decomposition-based recursive algorithms are introduced based on the classical Gauss-Newton (GN) and Levenberg-Marquardt (LM) algorithms in nonlinear unconstrained optimization or least squares problems. Instead of using the matrix inversion formula, recursive QR decomposition is employed, which is known to be numerically more stable in finite wordlength implementation. A family of p-A-QR-LS algorithms is then proposed to solve the LS problem resulting from the linearization of the NLS problem. It achieves different complexity-performance tradeoffs by retaining different number of diagonal plus off-diagonals (denoted by an integer p) of the triangular factor of the augmented data matrix. Simulation results on identifying a nonlinear perceptron are provided to illustrate the principle of the new algorithms. © 2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/45784
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhou, Yen_HK
dc.contributor.authorLau, WYen_HK
dc.date.accessioned2007-10-30T06:35:23Z-
dc.date.available2007-10-30T06:35:23Z-
dc.date.issued2005en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2005, p. 4333-4336en_HK
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45784-
dc.description.abstractThis paper proposes new approximate QR-based algorithms for recursive nonlinear least squares (NLS) estimation. Two QR decomposition-based recursive algorithms are introduced based on the classical Gauss-Newton (GN) and Levenberg-Marquardt (LM) algorithms in nonlinear unconstrained optimization or least squares problems. Instead of using the matrix inversion formula, recursive QR decomposition is employed, which is known to be numerically more stable in finite wordlength implementation. A family of p-A-QR-LS algorithms is then proposed to solve the LS problem resulting from the linearization of the NLS problem. It achieves different complexity-performance tradeoffs by retaining different number of diagonal plus off-diagonals (denoted by an integer p) of the triangular factor of the augmented data matrix. Simulation results on identifying a nonlinear perceptron are provided to illustrate the principle of the new algorithms. © 2005 IEEE.en_HK
dc.format.extent247793 bytes-
dc.format.extent27162 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systemsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©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.en_HK
dc.subjectElectronicsen_HK
dc.titleApproximate QR-based algorithms for recursive nonlinear least squares estiamtionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0271-4302&volume=5&spage=4333&epage=4336&date=2005&atitle=Approximate+QR-based+algorithms+for+recursive+nonlinear+least+squares+estimationen_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.emailZhou, Y: yizhou@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityZhou, Y=rp00213en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ISCAS.2005.1465590en_HK
dc.identifier.scopuseid_2-s2.0-67649097470en_HK
dc.identifier.hkuros103010-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-67649097470&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage4333en_HK
dc.identifier.epage4336en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhou, Y=55209555200en_HK
dc.identifier.scopusauthoridLau, WY=13608386400en_HK

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