Stability and convergence analysis of a neural model applied in nonlinear systems optimization
Autor(a) principal: | |
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Data de Publicação: | 2003 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/224969 |
Resumo: | A neural model for solving nonlinear optimization problems is presented in this paper. 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 network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology. © Springer-Verlag Berlin Heidelberg 2003. |
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Stability and convergence analysis of a neural model applied in nonlinear systems optimizationA neural model for solving nonlinear optimization problems is presented in this paper. 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 network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology. © Springer-Verlag Berlin Heidelberg 2003.State University of São Paulo UNESP Department of Electrical Engineering, CP 473, CEP 17033.360, Bauru/SPState University of São Paulo UNESP Department of Electrical Engineering, CP 473, CEP 17033.360, Bauru/SPUniversidade Estadual Paulista (UNESP)Nunes Da Silva, Ivan [UNESP]2022-04-28T20:21:39Z2022-04-28T20:21:39Z2003-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article189-197Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 2714, p. 189-197.1611-33490302-9743http://hdl.handle.net/11449/2249692-s2.0-35248861971Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-06-28T13:34:13Zoai:repositorio.unesp.br:11449/224969Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:24:49.990097Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Stability and convergence analysis of a neural model applied in nonlinear systems optimization |
title |
Stability and convergence analysis of a neural model applied in nonlinear systems optimization |
spellingShingle |
Stability and convergence analysis of a neural model applied in nonlinear systems optimization Nunes Da Silva, Ivan [UNESP] |
title_short |
Stability and convergence analysis of a neural model applied in nonlinear systems optimization |
title_full |
Stability and convergence analysis of a neural model applied in nonlinear systems optimization |
title_fullStr |
Stability and convergence analysis of a neural model applied in nonlinear systems optimization |
title_full_unstemmed |
Stability and convergence analysis of a neural model applied in nonlinear systems optimization |
title_sort |
Stability and convergence analysis of a neural model applied in nonlinear systems optimization |
author |
Nunes Da Silva, Ivan [UNESP] |
author_facet |
Nunes Da Silva, Ivan [UNESP] |
author_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Nunes Da Silva, Ivan [UNESP] |
description |
A neural model for solving nonlinear optimization problems is presented in this paper. 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 network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology. © Springer-Verlag Berlin Heidelberg 2003. |
publishDate |
2003 |
dc.date.none.fl_str_mv |
2003-01-01 2022-04-28T20:21:39Z 2022-04-28T20:21:39Z |
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 |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 2714, p. 189-197. 1611-3349 0302-9743 http://hdl.handle.net/11449/224969 2-s2.0-35248861971 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 2714, p. 189-197. 1611-3349 0302-9743 2-s2.0-35248861971 |
url |
http://hdl.handle.net/11449/224969 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
189-197 |
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_ |
1808128807044907008 |