Support vector method for robust ARMA system identification

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Mostrar el registro sencillo del ítem Rojo-Álvarez, José Luis Martínez-Ramón, Manel Prado-Cumplido, Mario de Artés Rodríguez, Antonio Figueiras-Vidal, Aníbal R. 2010-03-11T09:48:04Z 2010-03-11T09:48:04Z 2004-01
dc.identifier.bibliographicCitation IEEE transactions on signal processing, vol. 52, n. 1, p. 155-164. Jan. 2004
dc.identifier.issn 1053-587X
dc.description.abstract This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals andSVM-ARMA coefficients allows the linking of the fundamentals of SVM with several classical system identification methods. Additionally, the effect of outliers can be cancelled. Application examples show the performance of SVM-ARMA algorithm when it is compared with other system identification methods.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © IEEE
dc.subject.other Support vector machines
dc.subject.other ARMA modelling
dc.subject.other Cross-correlation
dc.subject.other System identification
dc.subject.other Time series.
dc.title Support vector method for robust ARMA system identification
dc.type article PeerReviewed
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi 10.1109/TSP.2003.820084
dc.rights.accessRights openAccess
dc.type.version publishedVersion
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 155
dc.identifier.publicationlastpage 164
dc.identifier.publicationtitle IEEE transactions on signal processing
dc.identifier.publicationvolume 52
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