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dc.date.accessioned 2012-10-16T14:38:48Z
dc.date.available 2012-10-16T14:38:48Z
dc.date.issued 2004
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/22497
dc.description.abstract Consider us the problem of time-varying parameter estimation. The most immediate and simple idea is to include a discounting procedure in an estimation algorithm i.e., a procedure for discarding (forgetting) old information. The most common way to do is to introduce an exponential forgetting factor (FF) into the corresponding estimation procedure (to see: Ljung and Gunnarson (1990)). In this paper, the authors going to describe a good enough estimator considering a system with nonstationary time variant properties with respect to input and output qualities. The techniques used are Instrumental Variable (IV) and Matrix Forgetting Factor (MFF). The results previously obtained by (Poznyak and Medel 1999a, 1999b) were the basis of this paper. The theoretical description illustrates the advantages with respect to others filters below cited. en
dc.language en es
dc.subject Filtering en
dc.subject Simulation es
dc.subject Parallel processing es
dc.subject estimation en
dc.subject Distributed es
dc.subject signal processing. en
dc.title Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain en
dc.type Objeto de conferencia es
sedici.creator.person Jesús Medel Juárez, José de es
sedici.creator.person Guevara López, Pedro es
sedici.creator.person Flores Rueda, Alberto es
sedici.description.note Eje: IV - Workshop de procesamiento distribuido y paralelo es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.relation.event X Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)