Wiener and Hammerstein uncertain models identification
- Autores
- Biagiola, Silvina Ines; Figueroa, Jose Luis
- Año de publicación
- 2009
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Block-oriented models have proved to be useful as simple nonlinear models for a vast number of applications. They are described as a cascade of linear dynamic and nonlinear static blocks. They have emerged as an appealing proposal due to their simplicity and the property of being valid over a larger operating region than a LTI model. In the description of these models, several approaches can be found in the literature to perform the identification process. In this sense, an important improvement is to achieve robust identification of blockoriented models to cope with the presence of uncertainty. In this article, we focus at two special and widely used types of uncertain block-oriented models: Hammerstein and Wiener models. They are assumed to be represented by a parametric representation. The approach herein followed allows to describe the uncertainty as a set of parameters which is found through the solution of an optimization problem. The identification algorithms are illustrated through a set of simple examples.
Fil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina - Materia
-
WIENER MODELS
HAMMERSTEIN MODELS
NONLINEAR DENTIFICATION
UNCERTAINTY - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/104451
Ver los metadatos del registro completo
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Wiener and Hammerstein uncertain models identificationBiagiola, Silvina InesFigueroa, Jose LuisWIENER MODELSHAMMERSTEIN MODELSNONLINEAR DENTIFICATIONUNCERTAINTYhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Block-oriented models have proved to be useful as simple nonlinear models for a vast number of applications. They are described as a cascade of linear dynamic and nonlinear static blocks. They have emerged as an appealing proposal due to their simplicity and the property of being valid over a larger operating region than a LTI model. In the description of these models, several approaches can be found in the literature to perform the identification process. In this sense, an important improvement is to achieve robust identification of blockoriented models to cope with the presence of uncertainty. In this article, we focus at two special and widely used types of uncertain block-oriented models: Hammerstein and Wiener models. They are assumed to be represented by a parametric representation. The approach herein followed allows to describe the uncertainty as a set of parameters which is found through the solution of an optimization problem. The identification algorithms are illustrated through a set of simple examples.Fil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaElsevier Science2009-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/104451Biagiola, Silvina Ines; Figueroa, Jose Luis; Wiener and Hammerstein uncertain models identification; Elsevier Science; Mathematics And Computers In Simulation; 79; 11; 7-2009; 3296-33130378-4754CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0378475409001281info:eu-repo/semantics/altIdentifier/doi/10.1016/j.matcom.2009.05.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:02:42Zoai:ri.conicet.gov.ar:11336/104451instacron: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 10:02:43.088CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Wiener and Hammerstein uncertain models identification |
title |
Wiener and Hammerstein uncertain models identification |
spellingShingle |
Wiener and Hammerstein uncertain models identification Biagiola, Silvina Ines WIENER MODELS HAMMERSTEIN MODELS NONLINEAR DENTIFICATION UNCERTAINTY |
title_short |
Wiener and Hammerstein uncertain models identification |
title_full |
Wiener and Hammerstein uncertain models identification |
title_fullStr |
Wiener and Hammerstein uncertain models identification |
title_full_unstemmed |
Wiener and Hammerstein uncertain models identification |
title_sort |
Wiener and Hammerstein uncertain models identification |
dc.creator.none.fl_str_mv |
Biagiola, Silvina Ines Figueroa, Jose Luis |
author |
Biagiola, Silvina Ines |
author_facet |
Biagiola, Silvina Ines Figueroa, Jose Luis |
author_role |
author |
author2 |
Figueroa, Jose Luis |
author2_role |
author |
dc.subject.none.fl_str_mv |
WIENER MODELS HAMMERSTEIN MODELS NONLINEAR DENTIFICATION UNCERTAINTY |
topic |
WIENER MODELS HAMMERSTEIN MODELS NONLINEAR DENTIFICATION UNCERTAINTY |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
Block-oriented models have proved to be useful as simple nonlinear models for a vast number of applications. They are described as a cascade of linear dynamic and nonlinear static blocks. They have emerged as an appealing proposal due to their simplicity and the property of being valid over a larger operating region than a LTI model. In the description of these models, several approaches can be found in the literature to perform the identification process. In this sense, an important improvement is to achieve robust identification of blockoriented models to cope with the presence of uncertainty. In this article, we focus at two special and widely used types of uncertain block-oriented models: Hammerstein and Wiener models. They are assumed to be represented by a parametric representation. The approach herein followed allows to describe the uncertainty as a set of parameters which is found through the solution of an optimization problem. The identification algorithms are illustrated through a set of simple examples. Fil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina Fil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina |
description |
Block-oriented models have proved to be useful as simple nonlinear models for a vast number of applications. They are described as a cascade of linear dynamic and nonlinear static blocks. They have emerged as an appealing proposal due to their simplicity and the property of being valid over a larger operating region than a LTI model. In the description of these models, several approaches can be found in the literature to perform the identification process. In this sense, an important improvement is to achieve robust identification of blockoriented models to cope with the presence of uncertainty. In this article, we focus at two special and widely used types of uncertain block-oriented models: Hammerstein and Wiener models. They are assumed to be represented by a parametric representation. The approach herein followed allows to describe the uncertainty as a set of parameters which is found through the solution of an optimization problem. The identification algorithms are illustrated through a set of simple examples. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-07 |
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/11336/104451 Biagiola, Silvina Ines; Figueroa, Jose Luis; Wiener and Hammerstein uncertain models identification; Elsevier Science; Mathematics And Computers In Simulation; 79; 11; 7-2009; 3296-3313 0378-4754 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/104451 |
identifier_str_mv |
Biagiola, Silvina Ines; Figueroa, Jose Luis; Wiener and Hammerstein uncertain models identification; Elsevier Science; Mathematics And Computers In Simulation; 79; 11; 7-2009; 3296-3313 0378-4754 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://www.sciencedirect.com/science/article/abs/pii/S0378475409001281 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.matcom.2009.05.004 |
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 |
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 |
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CONICET Digital (CONICET) |
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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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13.070432 |