Recursive identification of time-varying Hammerstein systems with matrix forgetting

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dc.contributor.authorDokoupil, Jakubcs
dc.contributor.authorVáclavek, Pavelcs
dc.coverage.issue5cs
dc.coverage.volume68cs
dc.date.issued2023-05-01cs
dc.description.abstractThe real-time estimation of the time-varying Hammerstein system by using a noniterative learning schema is considered and extended to incorporate a matrix forgetting factor. The estimation is cast in a variational-Bayes framework to best emulate the original posterior distribution of the parameters within the set of distributions with feasible moments. The recursive concept we propose approximates the exact posterior comprising undistorted information about the estimated parameters. In many practical settings, the incomplete model of parameter variations is compensated by forgetting of obsolete information. As a rule, the forgetting operation is initiated by the inclusion of an appropriate prediction alternative into the time update. It is shown that the careful formulation of the prediction alternative, which relies on Bayesian conditioning, results in partial forgetting. This article inspects two options with respect to the order of the conditioning in the posterior, which proves vital in the successful localization of the source of inconsistency in the data-generating process. The geometric mean of the discussed alternatives then modifies recursive learning through the matrix forgetting factor. We adopt the decision-making approach to revisit the posterior uncertainty by dynamically allocating the probability to each of the prediction alternatives to be combined.en
dc.formattextcs
dc.format.extent3078-3085cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE TRANSACTIONS ON AUTOMATIC CONTROL. 2023, vol. 68, issue 5, p. 3078-3085.en
dc.identifier.doi10.1109/TAC.2022.3188478cs
dc.identifier.issn0018-9286cs
dc.identifier.orcid0000-0001-7505-8571cs
dc.identifier.orcid0000-0001-8624-5874cs
dc.identifier.other182676cs
dc.identifier.researcheridA-7125-2013cs
dc.identifier.researcheridA-3448-2009cs
dc.identifier.scopus55807219000cs
dc.identifier.scopus8448897700cs
dc.identifier.urihttp://hdl.handle.net/11012/209568
dc.language.isoencs
dc.publisherIEEEcs
dc.relationEuropean Union (EU) & "Horizon 2020"
dc.relation.ispartofIEEE TRANSACTIONS ON AUTOMATIC CONTROLcs
dc.relation.projectIdinfo:eu-repo/grantAgreement/EC/H2020/857306/EU//RICAIP
dc.relation.urihttps://ieeexplore.ieee.org/document/9815531cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0018-9286/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectHammerstein modelen
dc.subjectmatrix forgetting factoren
dc.subjectparameter estimationen
dc.subjectvariational Bayes.en
dc.titleRecursive identification of time-varying Hammerstein systems with matrix forgettingen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-182676en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:33en
sync.item.modts2025.01.17 15:35:08en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav automatizace a měřicí technikycs
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Kybernetika a robotikacs
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