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- Publisher Website: 10.1016/j.automatica.2018.05.015
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Article: Tensor network subspace identification of polynomial state space models
Title | Tensor network subspace identification of polynomial state space models |
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Authors | |
Keywords | Identification methods Linear/nonlinear models MIMO Subspace methods System identification |
Issue Date | 2018 |
Publisher | Elsevier. 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? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/262187 |
ISSN | 2023 Impact Factor: 4.8 2023 SCImago Journal Rankings: 3.502 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Batselier, K | - |
dc.contributor.author | Ko, C-Y | - |
dc.contributor.author | Wong, N | - |
dc.date.accessioned | 2018-09-28T04:54:48Z | - |
dc.date.available | 2018-09-28T04:54:48Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Automatica, 2018, v. 95, p. 187-196 | - |
dc.identifier.issn | 0005-1098 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262187 | - |
dc.description.abstract | This 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.language | eng | - |
dc.publisher | Elsevier. The Journal's web site is located at http://www.elsevier.com/locate/automatica | - |
dc.relation.ispartof | Automatica | - |
dc.subject | Identification methods | - |
dc.subject | Linear/nonlinear models | - |
dc.subject | MIMO | - |
dc.subject | Subspace methods | - |
dc.subject | System identification | - |
dc.title | Tensor network subspace identification of polynomial state space models | - |
dc.type | Article | - |
dc.identifier.email | Batselier, K: kbatseli@HKUCC-COM.hku.hk | - |
dc.identifier.email | Wong, N: nwong@eee.hku.hk | - |
dc.identifier.authority | Wong, N=rp00190 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.automatica.2018.05.015 | - |
dc.identifier.scopus | eid_2-s2.0-85047742558 | - |
dc.identifier.hkuros | 292458 | - |
dc.identifier.volume | 95 | - |
dc.identifier.spage | 187 | - |
dc.identifier.epage | 196 | - |
dc.identifier.isi | WOS:000441853900020 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0005-1098 | - |