Nonlinear optimization using a modified Hopfield model

Detalhes bibliográficos
Autor(a) principal: da Silva, I. N.
Data de Publicação: 1998
Outros Autores: de Arruda, LVR, do Amaral, W. C.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IJCNN.1998.686022
http://hdl.handle.net/11449/33563
Resumo: Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving constrained nonlinear optimization problems. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach.
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spelling Nonlinear optimization using a modified Hopfield modelSystems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving constrained nonlinear optimization problems. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach.Univ São Paulo, UNESP FET DEE, Bauru, SP, BrazilUniv São Paulo, UNESP FET DEE, Bauru, SP, BrazilInstitute of Electrical and Electronics Engineers (IEEE)Universidade Estadual Paulista (Unesp)da Silva, I. N.de Arruda, LVRdo Amaral, W. C.2014-05-20T15:22:37Z2014-05-20T15:22:37Z1998-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1629-1633http://dx.doi.org/10.1109/IJCNN.1998.686022IEEE World Congress on Computational Intelligence. New York: IEEE, p. 1629-1633, 1998.http://hdl.handle.net/11449/3356310.1109/IJCNN.1998.686022WOS:000074493400298Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE World Congress on Computational Intelligenceinfo:eu-repo/semantics/openAccess2021-10-23T21:41:23Zoai:repositorio.unesp.br:11449/33563Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:41:23Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Nonlinear optimization using a modified Hopfield model
title Nonlinear optimization using a modified Hopfield model
spellingShingle Nonlinear optimization using a modified Hopfield model
da Silva, I. N.
title_short Nonlinear optimization using a modified Hopfield model
title_full Nonlinear optimization using a modified Hopfield model
title_fullStr Nonlinear optimization using a modified Hopfield model
title_full_unstemmed Nonlinear optimization using a modified Hopfield model
title_sort Nonlinear optimization using a modified Hopfield model
author da Silva, I. N.
author_facet da Silva, I. N.
de Arruda, LVR
do Amaral, W. C.
author_role author
author2 de Arruda, LVR
do Amaral, W. C.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv da Silva, I. N.
de Arruda, LVR
do Amaral, W. C.
description Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving constrained nonlinear optimization problems. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach.
publishDate 1998
dc.date.none.fl_str_mv 1998-01-01
2014-05-20T15:22:37Z
2014-05-20T15:22:37Z
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.1998.686022
IEEE World Congress on Computational Intelligence. New York: IEEE, p. 1629-1633, 1998.
http://hdl.handle.net/11449/33563
10.1109/IJCNN.1998.686022
WOS:000074493400298
url http://dx.doi.org/10.1109/IJCNN.1998.686022
http://hdl.handle.net/11449/33563
identifier_str_mv IEEE World Congress on Computational Intelligence. New York: IEEE, p. 1629-1633, 1998.
10.1109/IJCNN.1998.686022
WOS:000074493400298
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv IEEE World Congress on Computational Intelligence
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1629-1633
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv Web of Science
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
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