Stability analysis of adaptive filters with regression vector nonlinearities

Autores
Rey Vega, Leonardo Javier; Rey, Hernan; Benesty, Jacob
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present a unified framework to analyze the mean and mean-square stability of a large class of adaptive filters. We do this without obtaining a full transient model, allowing us to acquire sufficient conditions on the stability without assuming a given statistical distribution for the input regressors. We also apply the proposed framework to some popular adaptive filtering schemes, showing that in some cases the sufficient conditions derived are very tight and even necessary too.
Fil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Rey, Hernan. Universidad de Buenos Aires; Argentina
Fil: Benesty, Jacob. Centre Armand-frappier Santé Biotechnologie ; Institut National de Recherche Scientifique; . Université du Québec a Montreal; Canadá
Materia
ADAPTIVE FILTERS
MEAN STABILITY
MEAN-SQUARE STABILITY
STEP-SIZE PARAMETER
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/192917
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/192917
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Stability analysis of adaptive filters with regression vector nonlinearitiesRey Vega, Leonardo JavierRey, HernanBenesty, JacobADAPTIVE FILTERSMEAN STABILITYMEAN-SQUARE STABILITYSTEP-SIZE PARAMETERhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2We present a unified framework to analyze the mean and mean-square stability of a large class of adaptive filters. We do this without obtaining a full transient model, allowing us to acquire sufficient conditions on the stability without assuming a given statistical distribution for the input regressors. We also apply the proposed framework to some popular adaptive filtering schemes, showing that in some cases the sufficient conditions derived are very tight and even necessary too.Fil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Rey, Hernan. Universidad de Buenos Aires; ArgentinaFil: Benesty, Jacob. Centre Armand-frappier Santé Biotechnologie ; Institut National de Recherche Scientifique; . Université du Québec a Montreal; CanadáElsevier Science2011-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/192917Rey Vega, Leonardo Javier; Rey, Hernan; Benesty, Jacob; Stability analysis of adaptive filters with regression vector nonlinearities; Elsevier Science; Signal Processing; 91; 8; 8-2011; 2091-21000165-1684CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0165168411000934info:eu-repo/semantics/altIdentifier/doi/10.1016/j.sigpro.2011.03.018info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:50:39Zoai:ri.conicet.gov.ar:11336/192917instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:50:39.442CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Stability analysis of adaptive filters with regression vector nonlinearities
title Stability analysis of adaptive filters with regression vector nonlinearities
spellingShingle Stability analysis of adaptive filters with regression vector nonlinearities
Rey Vega, Leonardo Javier
ADAPTIVE FILTERS
MEAN STABILITY
MEAN-SQUARE STABILITY
STEP-SIZE PARAMETER
title_short Stability analysis of adaptive filters with regression vector nonlinearities
title_full Stability analysis of adaptive filters with regression vector nonlinearities
title_fullStr Stability analysis of adaptive filters with regression vector nonlinearities
title_full_unstemmed Stability analysis of adaptive filters with regression vector nonlinearities
title_sort Stability analysis of adaptive filters with regression vector nonlinearities
dc.creator.none.fl_str_mv Rey Vega, Leonardo Javier
Rey, Hernan
Benesty, Jacob
author Rey Vega, Leonardo Javier
author_facet Rey Vega, Leonardo Javier
Rey, Hernan
Benesty, Jacob
author_role author
author2 Rey, Hernan
Benesty, Jacob
author2_role author
author
dc.subject.none.fl_str_mv ADAPTIVE FILTERS
MEAN STABILITY
MEAN-SQUARE STABILITY
STEP-SIZE PARAMETER
topic ADAPTIVE FILTERS
MEAN STABILITY
MEAN-SQUARE STABILITY
STEP-SIZE PARAMETER
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv We present a unified framework to analyze the mean and mean-square stability of a large class of adaptive filters. We do this without obtaining a full transient model, allowing us to acquire sufficient conditions on the stability without assuming a given statistical distribution for the input regressors. We also apply the proposed framework to some popular adaptive filtering schemes, showing that in some cases the sufficient conditions derived are very tight and even necessary too.
Fil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Rey, Hernan. Universidad de Buenos Aires; Argentina
Fil: Benesty, Jacob. Centre Armand-frappier Santé Biotechnologie ; Institut National de Recherche Scientifique; . Université du Québec a Montreal; Canadá
description We present a unified framework to analyze the mean and mean-square stability of a large class of adaptive filters. We do this without obtaining a full transient model, allowing us to acquire sufficient conditions on the stability without assuming a given statistical distribution for the input regressors. We also apply the proposed framework to some popular adaptive filtering schemes, showing that in some cases the sufficient conditions derived are very tight and even necessary too.
publishDate 2011
dc.date.none.fl_str_mv 2011-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/192917
Rey Vega, Leonardo Javier; Rey, Hernan; Benesty, Jacob; Stability analysis of adaptive filters with regression vector nonlinearities; Elsevier Science; Signal Processing; 91; 8; 8-2011; 2091-2100
0165-1684
CONICET Digital
CONICET
url http://hdl.handle.net/11336/192917
identifier_str_mv Rey Vega, Leonardo Javier; Rey, Hernan; Benesty, Jacob; Stability analysis of adaptive filters with regression vector nonlinearities; Elsevier Science; Signal Processing; 91; 8; 8-2011; 2091-2100
0165-1684
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0165168411000934
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.sigpro.2011.03.018
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613560752144384
score 13.070432

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