A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

Detalhes bibliográficos
Autor(a) principal: Nunes Da Silva, I. [UNESP]
Data de Publicação: 2000
Outros Autores: Nunes De Souza, A. [UNESP], Bordon, M. E. [UNESP]
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.
id UNSP_a7c11b1001e462c30f9348f918489130
oai_identifier_str oai:repositorio.unesp.br:11449/220524
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling 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

Registros relacionados