Volterra-type models for nonlinear systems identification

Autores
Schmidt, Christian Andrés; Biagiola, Silvina Ines; Cousseau, Juan Edmundo; Figueroa, Jose Luis
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, multi-input multi-output (MIMO) nonlinear process identification is dealt with. In particular, two Volterra-type models are discussed in the context of system identification. These models are: Memory Polynomial (MP) and Modified Generalized Memory Polynomial (MGMP), which can be considered as a generalization of Hammerstein and Wiener models, respectively. Both of them are appealing representations as they allow to describe larger model sets with less parametric complexity. Simulation example is given to illustrate the quality of the obtained models.
Fil: Schmidt, Christian Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
Fil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
Fil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
Fil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
Materia
Nonlinear Identification
Volterra-Type Models
Wiener Model
Hammerstein Model
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:11336/11777
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Volterra-type models for nonlinear systems identificationSchmidt, Christian AndrésBiagiola, Silvina InesCousseau, Juan EdmundoFigueroa, Jose LuisNonlinear IdentificationVolterra-Type ModelsWiener ModelHammerstein Modelhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this work, multi-input multi-output (MIMO) nonlinear process identification is dealt with. In particular, two Volterra-type models are discussed in the context of system identification. These models are: Memory Polynomial (MP) and Modified Generalized Memory Polynomial (MGMP), which can be considered as a generalization of Hammerstein and Wiener models, respectively. Both of them are appealing representations as they allow to describe larger model sets with less parametric complexity. Simulation example is given to illustrate the quality of the obtained models.Fil: Schmidt, Christian Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; ArgentinaFil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; ArgentinaFil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; ArgentinaFil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; ArgentinaElsevier Science Inc2014-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/11777Schmidt, Christian Andrés; Biagiola, Silvina Ines; Cousseau, Juan Edmundo; Figueroa, Jose Luis; Volterra-type models for nonlinear systems identification; Elsevier Science Inc; Applied Mathematical Modelling; 38; 9-10; 5-2014; 2414-24210307-904Xenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0307904X13006537info:eu-repo/semantics/altIdentifier/doi/10.1016/j.apm.2013.10.041info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:04:02Zoai:ri.conicet.gov.ar:11336/11777instacron: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:34982025-09-10 13:04:02.852CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Volterra-type models for nonlinear systems identification
title Volterra-type models for nonlinear systems identification
spellingShingle Volterra-type models for nonlinear systems identification
Schmidt, Christian Andrés
Nonlinear Identification
Volterra-Type Models
Wiener Model
Hammerstein Model
title_short Volterra-type models for nonlinear systems identification
title_full Volterra-type models for nonlinear systems identification
title_fullStr Volterra-type models for nonlinear systems identification
title_full_unstemmed Volterra-type models for nonlinear systems identification
title_sort Volterra-type models for nonlinear systems identification
dc.creator.none.fl_str_mv Schmidt, Christian Andrés
Biagiola, Silvina Ines
Cousseau, Juan Edmundo
Figueroa, Jose Luis
author Schmidt, Christian Andrés
author_facet Schmidt, Christian Andrés
Biagiola, Silvina Ines
Cousseau, Juan Edmundo
Figueroa, Jose Luis
author_role author
author2 Biagiola, Silvina Ines
Cousseau, Juan Edmundo
Figueroa, Jose Luis
author2_role author
author
author
dc.subject.none.fl_str_mv Nonlinear Identification
Volterra-Type Models
Wiener Model
Hammerstein Model
topic Nonlinear Identification
Volterra-Type Models
Wiener Model
Hammerstein Model
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work, multi-input multi-output (MIMO) nonlinear process identification is dealt with. In particular, two Volterra-type models are discussed in the context of system identification. These models are: Memory Polynomial (MP) and Modified Generalized Memory Polynomial (MGMP), which can be considered as a generalization of Hammerstein and Wiener models, respectively. Both of them are appealing representations as they allow to describe larger model sets with less parametric complexity. Simulation example is given to illustrate the quality of the obtained models.
Fil: Schmidt, Christian Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
Fil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
Fil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
Fil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
description In this work, multi-input multi-output (MIMO) nonlinear process identification is dealt with. In particular, two Volterra-type models are discussed in the context of system identification. These models are: Memory Polynomial (MP) and Modified Generalized Memory Polynomial (MGMP), which can be considered as a generalization of Hammerstein and Wiener models, respectively. Both of them are appealing representations as they allow to describe larger model sets with less parametric complexity. Simulation example is given to illustrate the quality of the obtained models.
publishDate 2014
dc.date.none.fl_str_mv 2014-05
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/11336/11777
Schmidt, Christian Andrés; Biagiola, Silvina Ines; Cousseau, Juan Edmundo; Figueroa, Jose Luis; Volterra-type models for nonlinear systems identification; Elsevier Science Inc; Applied Mathematical Modelling; 38; 9-10; 5-2014; 2414-2421
0307-904X
url http://hdl.handle.net/11336/11777
identifier_str_mv Schmidt, Christian Andrés; Biagiola, Silvina Ines; Cousseau, Juan Edmundo; Figueroa, Jose Luis; Volterra-type models for nonlinear systems identification; Elsevier Science Inc; Applied Mathematical Modelling; 38; 9-10; 5-2014; 2414-2421
0307-904X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0307904X13006537
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.apm.2013.10.041
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
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Inc
publisher.none.fl_str_mv Elsevier Science Inc
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
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score 13.004268

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