A novel approach based on recurrent neural networks applied to nonlinear systems optimization
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808128600363237376 |