The performance surface in nonlinear mean square estimation: application to active noise control problems with correlated signals
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , |
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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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 |
_version_ |
1754824563894517760 |