Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain

Autores
Jesús Medel Juárez, José de; Guevara López, Pedro; Flores Rueda, Alberto
Año de publicación
2004
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
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.
Eje: IV - Workshop de procesamiento distribuido y paralelo
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Filtering
estimation
signal processing.
Simulation
Parallel processing
Distributed
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/22497
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/22497
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gainJesús Medel Juárez, José deGuevara López, PedroFlores Rueda, AlbertoCiencias InformáticasFilteringestimationsignal processing.SimulationParallel processingDistributedConsider 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.Eje: IV - Workshop de procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI)2004info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/22497enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:02Zoai:sedici.unlp.edu.ar:10915/22497Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:03.268SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
title Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
spellingShingle Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
Jesús Medel Juárez, José de
Ciencias Informáticas
Filtering
estimation
signal processing.
Simulation
Parallel processing
Distributed
title_short Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
title_full Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
title_fullStr Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
title_full_unstemmed Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
title_sort Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
dc.creator.none.fl_str_mv Jesús Medel Juárez, José de
Guevara López, Pedro
Flores Rueda, Alberto
author Jesús Medel Juárez, José de
author_facet Jesús Medel Juárez, José de
Guevara López, Pedro
Flores Rueda, Alberto
author_role author
author2 Guevara López, Pedro
Flores Rueda, Alberto
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Filtering
estimation
signal processing.
Simulation
Parallel processing
Distributed
topic Ciencias Informáticas
Filtering
estimation
signal processing.
Simulation
Parallel processing
Distributed
dc.description.none.fl_txt_mv 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.
Eje: IV - Workshop de procesamiento distribuido y paralelo
Red de Universidades con Carreras en Informática (RedUNCI)
description 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.
publishDate 2004
dc.date.none.fl_str_mv 2004
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22497
url http://sedici.unlp.edu.ar/handle/10915/22497
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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