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Online sliding-window methods for process model adaptation

dc.contributor.authorFerreira, P. M.
dc.contributor.authorRuano, Antonio
dc.date.accessioned2013-02-06T14:20:11Z
dc.date.available2013-02-06T14:20:11Z
dc.date.issued2009
dc.date.updated2013-01-26T17:41:09Z
dc.description.abstractOnline learning algorithms are needed when the process to be modeled is time varying or when it is impossible to obtain offline data that cover the whole operating region. To minimize the problems of parameter shadowing and interference, sliding-window-based algorithms are used. It is shown that, by using a sliding-window policy that enforces the novelty of the data it stores and by using a procedure to prevent unnecessary parameter updates, the performance achieved is improved over a first-in–first-out (FIFO) policy with fixed interval parameter updates. Important savings in computational effort are also obtained.por
dc.identifier.citationFerreira, P. M.; Ruano, A. E. Online Sliding-Window Methods for Process Model Adaptation, IEEE Transactions on Instrumentation and Measurement, 58, 9, 3012-3020, 2009.por
dc.identifier.doihttp://dx.doi.org/10.1109/TIM.2009.2016818
dc.identifier.issn0018-9456
dc.identifier.otherAUT: ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/2232
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIEEEpor
dc.subjectAdaptive systemspor
dc.subjectFeedforward neural networkspor
dc.subjectLearning systemspor
dc.subjectModelingpor
dc.subjectNonlinear systemspor
dc.titleOnline sliding-window methods for process model adaptationpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage3020por
oaire.citation.issue9por
oaire.citation.startPage3012por
oaire.citation.titleIEEE Transactions on Instrumentation and Measurementpor
oaire.citation.volume58por
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
rcaap.rightsrestrictedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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