Recursive identification of the ARARX model based on the variational Bayes method

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dc.contributor.authorDokoupil, Jakubcs
dc.contributor.authorVáclavek, Pavelcs
dc.date.issued2023-12-13cs
dc.description.abstractBayesian parameter estimation of autoregressive (AR) with exogenous input (X) systems in the presence of colored model noise is addressed. The stochastic system under consideration is driven by colored noise that arises from passing an initially white noise through an AR filter. Owing to the additional AR filter, the ARARX schema provides more flexibility than the ARX one. The gained flexibility is countered by the fact that the ARARX system is no longer linear-in-parameters unless the white noise components or the AR noise filter are available. This paper analyzes the problem of estimating the unknown coefficients of the ARARX system and the model noise precision under conditions where the AR noise filter is both available and unavailable. While the former condition reduces the estimation problem to standard linear least squares, the latter one gives rise to an analytically intractable estimation problem. The intractability is resolved by the distributional approximation technique based on the variational Bayes (VB) method.en
dc.formattextcs
dc.format.extent4215-4222cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citation62th IEEE Conference on Decision and Control. 2023, p. 4215-4222.en
dc.identifier.doi10.1109/CDC49753.2023.10383518cs
dc.identifier.isbn979-8-3503-0124-3cs
dc.identifier.orcid0000-0001-7505-8571cs
dc.identifier.orcid0000-0001-8624-5874cs
dc.identifier.other186745cs
dc.identifier.researcheridA-7125-2013cs
dc.identifier.researcheridA-3448-2009cs
dc.identifier.scopus55807219000cs
dc.identifier.scopus8448897700cs
dc.identifier.urihttp://hdl.handle.net/11012/244722
dc.language.isoencs
dc.publisherIEEEcs
dc.relationEuropean Union (EU) & "Horizon 2020"
dc.relation.ispartof62th IEEE Conference on Decision and Controlcs
dc.relation.projectIdinfo:eu-repo/grantAgreement/EC/H2020/857306/EU//RICAIP
dc.relation.urihttps://ieeexplore.ieee.org/document/10383518cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.subjectARARX systemen
dc.subjectVariational Bayes methoden
dc.subjectnormal-Wishart distributionen
dc.titleRecursive identification of the ARARX model based on the variational Bayes methoden
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-186745en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:34en
sync.item.modts2025.01.17 16:41:46en
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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