A barrier method for constrained nonlinear optimization using a modified Hopfield network
Autor(a) principal: | |
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Data de Publicação: | 2001 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN.2001.938425 http://hdl.handle.net/11449/66422 |
Resumo: | The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach. |
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spelling |
A barrier method for constrained nonlinear optimization using a modified Hopfield networkComputer simulationErrorsMathematical modelsOptimizationParameter estimationBarrier methodConstrained nonlinear optimizationEquilibrium pointModified Hopfield networkNonlinear modelUnknown but bounded errorsValid subspace techniqueNeural networksThe ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.State University of São Paulo Department of Electrical Engineering, CP 473, CEP 17033-360Universidade Estadual Paulista (Unesp)Silva, I. N. daUlson, Jose Alfredo Covolan [UNESP]Souza, A. N. de2014-05-27T11:20:13Z2014-05-27T11:20:13Z2001-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1744-1749http://dx.doi.org/10.1109/IJCNN.2001.938425Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 1744-1749.http://hdl.handle.net/11449/6642210.1109/IJCNN.2001.938425WOS:0001727848003102-s2.0-003486295245170571214622588212775960494686Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2024-06-28T13:34:42Zoai:repositorio.unesp.br:11449/66422Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:11:47.387641Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A barrier method for constrained nonlinear optimization using a modified Hopfield network |
title |
A barrier method for constrained nonlinear optimization using a modified Hopfield network |
spellingShingle |
A barrier method for constrained nonlinear optimization using a modified Hopfield network Silva, I. N. da Computer simulation Errors Mathematical models Optimization Parameter estimation Barrier method Constrained nonlinear optimization Equilibrium point Modified Hopfield network Nonlinear model Unknown but bounded errors Valid subspace technique Neural networks |
title_short |
A barrier method for constrained nonlinear optimization using a modified Hopfield network |
title_full |
A barrier method for constrained nonlinear optimization using a modified Hopfield network |
title_fullStr |
A barrier method for constrained nonlinear optimization using a modified Hopfield network |
title_full_unstemmed |
A barrier method for constrained nonlinear optimization using a modified Hopfield network |
title_sort |
A barrier method for constrained nonlinear optimization using a modified Hopfield network |
author |
Silva, I. N. da |
author_facet |
Silva, I. N. da Ulson, Jose Alfredo Covolan [UNESP] Souza, A. N. de |
author_role |
author |
author2 |
Ulson, Jose Alfredo Covolan [UNESP] Souza, A. N. de |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Silva, I. N. da Ulson, Jose Alfredo Covolan [UNESP] Souza, A. N. de |
dc.subject.por.fl_str_mv |
Computer simulation Errors Mathematical models Optimization Parameter estimation Barrier method Constrained nonlinear optimization Equilibrium point Modified Hopfield network Nonlinear model Unknown but bounded errors Valid subspace technique Neural networks |
topic |
Computer simulation Errors Mathematical models Optimization Parameter estimation Barrier method Constrained nonlinear optimization Equilibrium point Modified Hopfield network Nonlinear model Unknown but bounded errors Valid subspace technique Neural networks |
description |
The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach. |
publishDate |
2001 |
dc.date.none.fl_str_mv |
2001-01-01 2014-05-27T11:20:13Z 2014-05-27T11:20:13Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IJCNN.2001.938425 Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 1744-1749. http://hdl.handle.net/11449/66422 10.1109/IJCNN.2001.938425 WOS:000172784800310 2-s2.0-0034862952 4517057121462258 8212775960494686 |
url |
http://dx.doi.org/10.1109/IJCNN.2001.938425 http://hdl.handle.net/11449/66422 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 1744-1749. 10.1109/IJCNN.2001.938425 WOS:000172784800310 2-s2.0-0034862952 4517057121462258 8212775960494686 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1744-1749 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129296932274176 |