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Article: Tensor network subspace identification of polynomial state space models

TitleTensor network subspace identification of polynomial state space models
Authors
KeywordsIdentification methods
Linear/nonlinear models
MIMO
Subspace methods
System identification
Issue Date2018
PublisherElsevier. The Journal's web site is located at http://www.elsevier.com/locate/automatica
Citation
Automatica, 2018, v. 95, p. 187-196 How to Cite?
AbstractThis article introduces a tensor network subspace algorithm for the identification of specific polynomial state space models. The polynomial nonlinearity in the state space model is completely written in terms of a tensor network, thus avoiding the curse of dimensionality. We also prove how the block Hankel data matrices in the subspace method can be exactly represented by low rank tensor networks, reducing the computational and storage complexity significantly. The performance and accuracy of our subspace identification algorithm are illustrated by experiments, showing that our tensor network implementation identifies a seventh degree polynomial state space model around 20 times faster than the standard matrix implementation before the latter fails due to insufficient memory. The proposed algorithm is also robust with respect to noise and therefore applicable to practical systems.
Persistent Identifierhttp://hdl.handle.net/10722/262187
ISSN
2017 Impact Factor: 6.126
2015 SCImago Journal Rankings: 4.315
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorBatselier, K-
dc.contributor.authorKo, C-Y-
dc.contributor.authorWong, N-
dc.date.accessioned2018-09-28T04:54:48Z-
dc.date.available2018-09-28T04:54:48Z-
dc.date.issued2018-
dc.identifier.citationAutomatica, 2018, v. 95, p. 187-196-
dc.identifier.issn0005-1098-
dc.identifier.urihttp://hdl.handle.net/10722/262187-
dc.description.abstractThis article introduces a tensor network subspace algorithm for the identification of specific polynomial state space models. The polynomial nonlinearity in the state space model is completely written in terms of a tensor network, thus avoiding the curse of dimensionality. We also prove how the block Hankel data matrices in the subspace method can be exactly represented by low rank tensor networks, reducing the computational and storage complexity significantly. The performance and accuracy of our subspace identification algorithm are illustrated by experiments, showing that our tensor network implementation identifies a seventh degree polynomial state space model around 20 times faster than the standard matrix implementation before the latter fails due to insufficient memory. The proposed algorithm is also robust with respect to noise and therefore applicable to practical systems.-
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.subjectLinear/nonlinear models-
dc.subjectMIMO-
dc.subjectSubspace methods-
dc.subjectSystem identification-
dc.titleTensor network subspace identification of polynomial state space models-
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.2018.05.015-
dc.identifier.scopuseid_2-s2.0-85047742558-
dc.identifier.hkuros292458-
dc.identifier.volume95-
dc.identifier.spage187-
dc.identifier.epage196-
dc.identifier.isiWOS:000441853900020-
dc.publisher.placeUnited Kingdom-

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