File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.automatica.2018.06.015
- Scopus: eid_2-s2.0-85048705159
- WOS: WOS:000441853900043
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Matrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification
Title | Matrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification |
---|---|
Authors | |
Keywords | Identification methods Kalman filters MIMO System identification Tensors |
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. 413-418 How to Cite? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/261768 |
ISSN | 2023 Impact Factor: 4.8 2023 SCImago Journal Rankings: 3.502 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Batselier, K | - |
dc.contributor.author | Wong, N | - |
dc.date.accessioned | 2018-09-28T04:47:33Z | - |
dc.date.available | 2018-09-28T04:47:33Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Automatica, 2018, v. 95, p. 413-418 | - |
dc.identifier.issn | 0005-1098 | - |
dc.identifier.uri | http://hdl.handle.net/10722/261768 | - |
dc.description.abstract | This 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.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 | Kalman filters | - |
dc.subject | MIMO | - |
dc.subject | System identification | - |
dc.subject | Tensors | - |
dc.title | Matrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification | - |
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.2018.06.015 | - |
dc.identifier.scopus | eid_2-s2.0-85048705159 | - |
dc.identifier.hkuros | 292460 | - |
dc.identifier.volume | 95 | - |
dc.identifier.spage | 413 | - |
dc.identifier.epage | 418 | - |
dc.identifier.isi | WOS:000441853900043 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0005-1098 | - |