The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals

Detalhes bibliográficos
Autor(a) principal: Costa,Márcio H.
Data de Publicação: 2002
Outros Autores: Bermudez,José C. M., Bershad,Neil J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Sba: Controle & Automação Sociedade Brasileira de Automatica
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592002000100008
Resumo: This paper investigates the properties of the performance surface for the problem of nonlinear mean-square estimation of a random sequence. The problem studied has direct application to the study of active noise control (ANC) systems when the transducers are driven into a nonlinear behavior. A deterministic expression is derived for the mean-square error (MSE) surface as a function of the system's degree of nonlinearity for Gaussian correlated input signals. It is shown how the presence of the nonlinearity deforms the MSE surface. It is demonstrated that the surface is unimodal, and the expression for the optimum weight vector is determined. The new results are then used to quantify the behavior of ANC systems employing the LMS adaptive algorithm. Important algorithm properties are derived from this study. Examples are presented which verify the analytical models derived.
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spelling The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signalsactive noise controladaptive filtersadaptive algorithmsnonlinear systemsestimation theoryThis paper investigates the properties of the performance surface for the problem of nonlinear mean-square estimation of a random sequence. The problem studied has direct application to the study of active noise control (ANC) systems when the transducers are driven into a nonlinear behavior. A deterministic expression is derived for the mean-square error (MSE) surface as a function of the system's degree of nonlinearity for Gaussian correlated input signals. It is shown how the presence of the nonlinearity deforms the MSE surface. It is demonstrated that the surface is unimodal, and the expression for the optimum weight vector is determined. The new results are then used to quantify the behavior of ANC systems employing the LMS adaptive algorithm. Important algorithm properties are derived from this study. Examples are presented which verify the analytical models derived.Sociedade Brasileira de Automática2002-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592002000100008Sba: Controle & Automação Sociedade Brasileira de Automatica v.13 n.1 2002reponame:Sba: Controle & Automação Sociedade Brasileira de Automaticainstname:Sociedade Brasileira de Automática (SBA)instacron:SBA10.1590/S0103-17592002000100008info:eu-repo/semantics/openAccessCosta,Márcio H.Bermudez,José C. M.Bershad,Neil J.eng2003-01-15T00:00:00Zoai:scielo:S0103-17592002000100008Revistahttps://www.sba.org.br/revista/PUBhttps://old.scielo.br/oai/scielo-oai.php||revista_sba@fee.unicamp.br1807-03450103-1759opendoar:2003-01-15T00:00Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA)false
dc.title.none.fl_str_mv The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
title The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
spellingShingle The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
Costa,Márcio H.
active noise control
adaptive filters
adaptive algorithms
nonlinear systems
estimation theory
title_short The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
title_full The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
title_fullStr The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
title_full_unstemmed The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
title_sort The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
author Costa,Márcio H.
author_facet Costa,Márcio H.
Bermudez,José C. M.
Bershad,Neil J.
author_role author
author2 Bermudez,José C. M.
Bershad,Neil J.
author2_role author
author
dc.contributor.author.fl_str_mv Costa,Márcio H.
Bermudez,José C. M.
Bershad,Neil J.
dc.subject.por.fl_str_mv active noise control
adaptive filters
adaptive algorithms
nonlinear systems
estimation theory
topic active noise control
adaptive filters
adaptive algorithms
nonlinear systems
estimation theory
description This paper investigates the properties of the performance surface for the problem of nonlinear mean-square estimation of a random sequence. The problem studied has direct application to the study of active noise control (ANC) systems when the transducers are driven into a nonlinear behavior. A deterministic expression is derived for the mean-square error (MSE) surface as a function of the system's degree of nonlinearity for Gaussian correlated input signals. It is shown how the presence of the nonlinearity deforms the MSE surface. It is demonstrated that the surface is unimodal, and the expression for the optimum weight vector is determined. The new results are then used to quantify the behavior of ANC systems employing the LMS adaptive algorithm. Important algorithm properties are derived from this study. Examples are presented which verify the analytical models derived.
publishDate 2002
dc.date.none.fl_str_mv 2002-04-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592002000100008
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592002000100008
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0103-17592002000100008
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Automática
publisher.none.fl_str_mv Sociedade Brasileira de Automática
dc.source.none.fl_str_mv Sba: Controle & Automação Sociedade Brasileira de Automatica v.13 n.1 2002
reponame:Sba: Controle & Automação Sociedade Brasileira de Automatica
instname:Sociedade Brasileira de Automática (SBA)
instacron:SBA
instname_str Sociedade Brasileira de Automática (SBA)
instacron_str SBA
institution SBA
reponame_str Sba: Controle & Automação Sociedade Brasileira de Automatica
collection Sba: Controle & Automação Sociedade Brasileira de Automatica
repository.name.fl_str_mv Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA)
repository.mail.fl_str_mv ||revista_sba@fee.unicamp.br
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