Parallel And Distributed Computational Multivariate Time Series Modeling In The State Space

dc.creatorBottura C.P.
dc.creatorBordon M.J.
dc.creatorBarreto G.
dc.creatorTamariz A.D.R.
dc.date2002
dc.date2015-06-30T16:41:52Z
dc.date2015-11-26T15:32:01Z
dc.date2015-06-30T16:41:52Z
dc.date2015-11-26T15:32:01Z
dc.date.accessioned2018-03-28T22:40:31Z
dc.date.available2018-03-28T22:40:31Z
dc.identifier
dc.identifierProceedings Of The American Control Conference. , v. 2, n. , p. 1466 - 1471, 2002.
dc.identifier7431619
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-0036056266&partnerID=40&md5=06f0c5db7ebacdda1b9799d78d7206e6
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/101610
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/101610
dc.identifier2-s2.0-0036056266
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1262382
dc.descriptionIn this paper a parallel and distributed computational procedure using a subspace method developed by Masanao Aoki for state space modeling of multivariate time series is proposed and implemented. The parallel solution of the Riccati equation due to the large computational effort it requires receives a special attention. For model evaluation, short time predictions, where a central role is played by a Kalman filtering approach are tested and some results are presented.
dc.description2
dc.description
dc.description1466
dc.description1471
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dc.languageen
dc.publisher
dc.relationProceedings of the American Control Conference
dc.rightsfechado
dc.sourceScopus
dc.titleParallel And Distributed Computational Multivariate Time Series Modeling In The State Space
dc.typeActas de congresos


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