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Article: Matrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification

TitleMatrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification
Authors
KeywordsIdentification methods
Kalman filters
MIMO
System identification
Tensors
Issue Date2018
PublisherElsevier. The Journal's web site is located at http://www.elsevier.com/locate/automatica
Citation
Automatica, 2018, v. 95, p. 413-418 How to Cite?
AbstractThis article extends the tensor network Kalman filter to matrix outputs with an application in recursive identification of discrete-time nonlinear multiple-input-multiple-output (MIMO) Volterra systems. This extension completely supersedes previous work, where only l scalar outputs were considered. The Kalman tensor equations are modified to accommodate for matrix outputs and their implementation using tensor networks is discussed. The MIMO Volterra system identification application requires the conversion of the output model matrix with a row-wise Kronecker product structure into its corresponding tensor network, for which we propose an efficient algorithm. Numerical experiments demonstrate both the efficacy of the proposed matrix conversion algorithm and the improved convergence of the Volterra kernel estimates when using matrix outputs.
Persistent Identifierhttp://hdl.handle.net/10722/261768
ISSN
2017 Impact Factor: 6.126
2015 SCImago Journal Rankings: 4.315
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorBatselier, K-
dc.contributor.authorWong, N-
dc.date.accessioned2018-09-28T04:47:33Z-
dc.date.available2018-09-28T04:47:33Z-
dc.date.issued2018-
dc.identifier.citationAutomatica, 2018, v. 95, p. 413-418-
dc.identifier.issn0005-1098-
dc.identifier.urihttp://hdl.handle.net/10722/261768-
dc.description.abstractThis article extends the tensor network Kalman filter to matrix outputs with an application in recursive identification of discrete-time nonlinear multiple-input-multiple-output (MIMO) Volterra systems. This extension completely supersedes previous work, where only l scalar outputs were considered. The Kalman tensor equations are modified to accommodate for matrix outputs and their implementation using tensor networks is discussed. The MIMO Volterra system identification application requires the conversion of the output model matrix with a row-wise Kronecker product structure into its corresponding tensor network, for which we propose an efficient algorithm. Numerical experiments demonstrate both the efficacy of the proposed matrix conversion algorithm and the improved convergence of the Volterra kernel estimates when using matrix outputs.-
dc.languageeng-
dc.publisherElsevier. The Journal's web site is located at http://www.elsevier.com/locate/automatica-
dc.relation.ispartofAutomatica-
dc.subjectIdentification methods-
dc.subjectKalman filters-
dc.subjectMIMO-
dc.subjectSystem identification-
dc.subjectTensors-
dc.titleMatrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification-
dc.typeArticle-
dc.identifier.emailBatselier, K: kbatseli@HKUCC-COM.hku.hk-
dc.identifier.emailWong, N: nwong@eee.hku.hk-
dc.identifier.authorityWong, N=rp00190-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.automatica.2018.06.015-
dc.identifier.scopuseid_2-s2.0-85048705159-
dc.identifier.hkuros292460-
dc.identifier.volume95-
dc.identifier.spage413-
dc.identifier.epage418-
dc.identifier.isiWOS:000441853900043-
dc.publisher.placeUnited Kingdom-

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