A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems
Autor(a) principal: | |
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Data de Publicação: | 2000 |
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/SBRN.2000.889741 http://hdl.handle.net/11449/220524 |
Resumo: | A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented. 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. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach. |
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Repositório Institucional da UNESP |
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A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systemsArtificial neural networksBiological system modelingComputational modelingComputer networksConstraint optimizationDesign optimizationEquationsFuzzy logicNeural networksNeuronsA neural network model for solving constrained nonlinear optimization problems with bounded variables is presented. 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. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.State University of São Paulo - UNESP Department of Electrical Engineering UNESP/FE/DEE, CP 473State University of São Paulo - UNESP Department of Electrical Engineering UNESP/FE/DEE, CP 473Universidade Estadual Paulista (UNESP)Nunes Da Silva, I. [UNESP]Nunes De Souza, A. [UNESP]Bordon, M. E. [UNESP]2022-04-28T19:02:26Z2022-04-28T19:02:26Z2000-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject213-218http://dx.doi.org/10.1109/SBRN.2000.889741Proceedings - Brazilian Symposium on Neural Networks, SBRN, v. 2000-January, p. 213-218.1522-4899http://hdl.handle.net/11449/22052410.1109/SBRN.2000.8897412-s2.0-84950240800Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - Brazilian Symposium on Neural Networks, SBRNinfo:eu-repo/semantics/openAccess2022-04-28T19:02:26Zoai:repositorio.unesp.br:11449/220524Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:52:04.302830Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems |
title |
A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems |
spellingShingle |
A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems Nunes Da Silva, I. [UNESP] Artificial neural networks Biological system modeling Computational modeling Computer networks Constraint optimization Design optimization Equations Fuzzy logic Neural networks Neurons |
title_short |
A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems |
title_full |
A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems |
title_fullStr |
A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems |
title_full_unstemmed |
A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems |
title_sort |
A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems |
author |
Nunes Da Silva, I. [UNESP] |
author_facet |
Nunes Da Silva, I. [UNESP] Nunes De Souza, A. [UNESP] Bordon, M. E. [UNESP] |
author_role |
author |
author2 |
Nunes De Souza, A. [UNESP] Bordon, M. E. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Nunes Da Silva, I. [UNESP] Nunes De Souza, A. [UNESP] Bordon, M. E. [UNESP] |
dc.subject.por.fl_str_mv |
Artificial neural networks Biological system modeling Computational modeling Computer networks Constraint optimization Design optimization Equations Fuzzy logic Neural networks Neurons |
topic |
Artificial neural networks Biological system modeling Computational modeling Computer networks Constraint optimization Design optimization Equations Fuzzy logic Neural networks Neurons |
description |
A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented. 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. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach. |
publishDate |
2000 |
dc.date.none.fl_str_mv |
2000-01-01 2022-04-28T19:02:26Z 2022-04-28T19:02:26Z |
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/SBRN.2000.889741 Proceedings - Brazilian Symposium on Neural Networks, SBRN, v. 2000-January, p. 213-218. 1522-4899 http://hdl.handle.net/11449/220524 10.1109/SBRN.2000.889741 2-s2.0-84950240800 |
url |
http://dx.doi.org/10.1109/SBRN.2000.889741 http://hdl.handle.net/11449/220524 |
identifier_str_mv |
Proceedings - Brazilian Symposium on Neural Networks, SBRN, v. 2000-January, p. 213-218. 1522-4899 10.1109/SBRN.2000.889741 2-s2.0-84950240800 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - Brazilian Symposium on Neural Networks, SBRN |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
213-218 |
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_ |
1808129131212177408 |