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On-line sliding-window Levenberg-Marquardt methods for neural network models

dc.contributor.authorFerreira, P. M.
dc.contributor.authorRuano, Antonio
dc.date.accessioned2013-02-07T14:16:16Z
dc.date.available2013-02-07T14:16:16Z
dc.date.issued2007
dc.date.updated2013-01-26T18:37:55Z
dc.description.abstractOn-line learning algorithms are needed when the process to be modeled is time-varying or when it is impossible to obtain off-line data that covers the whole operating region. To minimize the problems of parameter shadowing and interference, sliding-based algorithms are used. It is shown that, by using a sliding window policy that enforces the novelty of data stored in the sliding window, and by using a procedure to prevent unnecessary parameter updates, the performance achieved is improved over a FIFO policy with fixed parameter updates. Important savings in computational effort are also obtained.por
dc.identifier.citationFerreira, P. M.; Ruano, A. E. On-line sliding-window Levenberg-Marquardt methods for neural network models, Trabalho apresentado em 2007 IEEE International Symposium on Intelligent Signal Processing, In Proceedings of the 2007 IEEE International Symposium on Intelligent Signal Processing, Alcala de Henares, Spain, 2007.por
dc.identifier.doihttp://dx.doi.org/10.1109/WISP.2007.4447542
dc.identifier.isbn978-1-4244-0829-0
dc.identifier.otherAUT: ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/2252
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIEEEpor
dc.titleOn-line sliding-window Levenberg-Marquardt methods for neural network modelspor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceAlcala de Henares, Spainpor
oaire.citation.endPage6por
oaire.citation.startPage1por
oaire.citation.titleInternational Symposium on Intelligent Signal Processingpor
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
rcaap.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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