A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning

Detalhes bibliográficos
Autor(a) principal: Angélico,Bruno A.
Data de Publicação: 2013
Outros Autores: Mendonça,Márcio, Abrão,Taufik, Arruda,Lúcia Valéria R. de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa operacional (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382013000300007
Resumo: This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM) learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.
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spelling A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learningFuzzy Cognitive MapsParticle Swarm OptimizationGenetic Algorithmprocess controlstatistical convergence analysisThis work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM) learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.Sociedade Brasileira de Pesquisa Operacional2013-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382013000300007Pesquisa Operacional v.33 n.3 2013reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/S0101-74382013005000009info:eu-repo/semantics/openAccessAngélico,Bruno A.Mendonça,MárcioAbrão,TaufikArruda,Lúcia Valéria R. deeng2013-12-16T00:00:00Zoai:scielo:S0101-74382013000300007Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2013-12-16T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false
dc.title.none.fl_str_mv A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning
title A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning
spellingShingle A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning
Angélico,Bruno A.
Fuzzy Cognitive Maps
Particle Swarm Optimization
Genetic Algorithm
process control
statistical convergence analysis
title_short A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning
title_full A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning
title_fullStr A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning
title_full_unstemmed A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning
title_sort A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning
author Angélico,Bruno A.
author_facet Angélico,Bruno A.
Mendonça,Márcio
Abrão,Taufik
Arruda,Lúcia Valéria R. de
author_role author
author2 Mendonça,Márcio
Abrão,Taufik
Arruda,Lúcia Valéria R. de
author2_role author
author
author
dc.contributor.author.fl_str_mv Angélico,Bruno A.
Mendonça,Márcio
Abrão,Taufik
Arruda,Lúcia Valéria R. de
dc.subject.por.fl_str_mv Fuzzy Cognitive Maps
Particle Swarm Optimization
Genetic Algorithm
process control
statistical convergence analysis
topic Fuzzy Cognitive Maps
Particle Swarm Optimization
Genetic Algorithm
process control
statistical convergence analysis
description This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM) learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.
publishDate 2013
dc.date.none.fl_str_mv 2013-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382013000300007
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382013000300007
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0101-74382013005000009
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
dc.source.none.fl_str_mv Pesquisa Operacional v.33 n.3 2013
reponame:Pesquisa operacional (Online)
instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron:SOBRAPO
instname_str Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron_str SOBRAPO
institution SOBRAPO
reponame_str Pesquisa operacional (Online)
collection Pesquisa operacional (Online)
repository.name.fl_str_mv Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
repository.mail.fl_str_mv ||sobrapo@sobrapo.org.br
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