A novel approach based on recurrent neural networks applied to nonlinear systems optimization

Detalhes bibliográficos
Autor(a) principal: da Silva, Ivan Nunes
Data de Publicação: 2007
Outros Autores: do Amaral, Wagner Caradori, de Arruda, Lucia Valeria
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.apm.2005.08.007
http://hdl.handle.net/11449/8885
Resumo: This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.
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spelling A novel approach based on recurrent neural networks applied to nonlinear systems optimizationnonlinear optimization problemsrecurrent neural networksHopfield networksnonlinear programmingThis paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.São Paulo State Univ, Dept Elect Engn, UNESP, BR-17033360 Bauru, SP, BrazilSão Paulo State Univ, Dept Elect Engn, UNESP, BR-17033360 Bauru, SP, BrazilElsevier B.V.Universidade Estadual Paulista (Unesp)da Silva, Ivan Nunesdo Amaral, Wagner Caradoride Arruda, Lucia Valeria2014-05-20T13:27:12Z2014-05-20T13:27:12Z2007-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article78-92application/pdfhttp://dx.doi.org/10.1016/j.apm.2005.08.007Applied Mathematical Modelling. New York: Elsevier B.V., v. 31, n. 1, p. 78-92, 2007.0307-904Xhttp://hdl.handle.net/11449/888510.1016/j.apm.2005.08.007WOS:000242415200006WOS000242415200006.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Mathematical Modelling2.617info:eu-repo/semantics/openAccess2024-06-28T13:34:11Zoai:repositorio.unesp.br:11449/8885Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:02:59.584605Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A novel approach based on recurrent neural networks applied to nonlinear systems optimization
title A novel approach based on recurrent neural networks applied to nonlinear systems optimization
spellingShingle A novel approach based on recurrent neural networks applied to nonlinear systems optimization
da Silva, Ivan Nunes
nonlinear optimization problems
recurrent neural networks
Hopfield networks
nonlinear programming
title_short A novel approach based on recurrent neural networks applied to nonlinear systems optimization
title_full A novel approach based on recurrent neural networks applied to nonlinear systems optimization
title_fullStr A novel approach based on recurrent neural networks applied to nonlinear systems optimization
title_full_unstemmed A novel approach based on recurrent neural networks applied to nonlinear systems optimization
title_sort A novel approach based on recurrent neural networks applied to nonlinear systems optimization
author da Silva, Ivan Nunes
author_facet da Silva, Ivan Nunes
do Amaral, Wagner Caradori
de Arruda, Lucia Valeria
author_role author
author2 do Amaral, Wagner Caradori
de Arruda, Lucia Valeria
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv da Silva, Ivan Nunes
do Amaral, Wagner Caradori
de Arruda, Lucia Valeria
dc.subject.por.fl_str_mv nonlinear optimization problems
recurrent neural networks
Hopfield networks
nonlinear programming
topic nonlinear optimization problems
recurrent neural networks
Hopfield networks
nonlinear programming
description This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.
publishDate 2007
dc.date.none.fl_str_mv 2007-01-01
2014-05-20T13:27:12Z
2014-05-20T13:27:12Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.apm.2005.08.007
Applied Mathematical Modelling. New York: Elsevier B.V., v. 31, n. 1, p. 78-92, 2007.
0307-904X
http://hdl.handle.net/11449/8885
10.1016/j.apm.2005.08.007
WOS:000242415200006
WOS000242415200006.pdf
url http://dx.doi.org/10.1016/j.apm.2005.08.007
http://hdl.handle.net/11449/8885
identifier_str_mv Applied Mathematical Modelling. New York: Elsevier B.V., v. 31, n. 1, p. 78-92, 2007.
0307-904X
10.1016/j.apm.2005.08.007
WOS:000242415200006
WOS000242415200006.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Applied Mathematical Modelling
2.617
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 78-92
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
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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