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Optimization of a fuzzy logic controller for MR dampers using ANFIS

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Resumo:Fuzzy controllers have been successfully applied to a wide range of engineering problems due its robustness and the ability to deal with non-linear plants. Despite the inherent advantages of these controllers, there is no systematic technique for converting human knowledge into the rule base of a fuzzy inference system. Adaptive neuro-fuzzy inference system (ANFIS) is an artificial intelligence technique that has been successfully used for mapping input-output relationships based on available data sets, i.e., to automatically adjust a fuzzy inference system with a backpropagation algorithm based on training data. This paper presents the application of a ANFIS model to optimize the parameters of a fuzzy controller for structural control of a building structure using a MR damper. The results obtained with the neurofuzzy controller are compared with those of a passive control modes to assess the performance of the proposed control system in reducing the seismic response of the structure.
Autores principais:Braz-César, Manuel
Outros Autores:Barros, Rui
Assunto:Fuzzy control Semi-active control MR dampers Structural dynamics ANFIS
Ano:2015
País:Portugal
Tipo de documento:documento de conferência
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
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author Braz-César, Manuel
author2 Barros, Rui
author2_role author
author_facet Braz-César, Manuel
Barros, Rui
author_role author
contributor_name_str_mv Biblioteca Digital do IPB
country_str PT
creators_json_txt [{\"Person.name\":\"Braz-César, Manuel\",\"Person.identifier.orcid\":\"0000-0001-5640-0714\"},{\"Person.name\":\"Barros, Rui\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Biblioteca Digital do IPB
datacite.creators.creator.creatorName.fl_str_mv Braz-César, Manuel
Barros, Rui
datacite.date.Accepted.fl_str_mv 2015-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2018-05-02T09:52:52Z
datacite.date.embargoed.fl_str_mv 2018-05-02T09:52:52Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv Fuzzy control
Semi-active control
MR dampers
Structural dynamics
ANFIS
datacite.titles.title.fl_str_mv Optimization of a fuzzy logic controller for MR dampers using ANFIS
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.creator.none.fl_str_mv Braz-César, Manuel
Barros, Rui
dc.date.Accepted.fl_str_mv 2015-01-01T00:00:00Z
dc.date.available.fl_str_mv 2018-05-02T09:52:52Z
dc.date.embargoed.fl_str_mv 2018-05-02T09:52:52Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10198/17547
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv Universidade da Beira Interior
dc.rights.cclincense.fl_str_mv http://creativecommons.org/licenses/by/4.0/
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv Fuzzy control
Semi-active control
MR dampers
Structural dynamics
ANFIS
dc.title.fl_str_mv Optimization of a fuzzy logic controller for MR dampers using ANFIS
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_c94f
description Fuzzy controllers have been successfully applied to a wide range of engineering problems due its robustness and the ability to deal with non-linear plants. Despite the inherent advantages of these controllers, there is no systematic technique for converting human knowledge into the rule base of a fuzzy inference system. Adaptive neuro-fuzzy inference system (ANFIS) is an artificial intelligence technique that has been successfully used for mapping input-output relationships based on available data sets, i.e., to automatically adjust a fuzzy inference system with a backpropagation algorithm based on training data. This paper presents the application of a ANFIS model to optimize the parameters of a fuzzy controller for structural control of a building structure using a MR damper. The results obtained with the neurofuzzy controller are compared with those of a passive control modes to assess the performance of the proposed control system in reducing the seismic response of the structure.
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eu_rights_str_mv openAccess
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fulltext.url.fl_str_mv https://bibliotecadigital.ipb.pt/bitstreams/49e1a038-8d0b-4ecf-8d10-f1c30a3a3256/download
id ipb_4bf4bcde46ec4fbfbfb71cda0894c4b8
identifier.url.fl_str_mv http://hdl.handle.net/10198/17547
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institution Instituto Politécnico de Bragança
instname_str Instituto Politécnico de Bragança
language eng
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network_name_str Biblioteca Digital do IPB
oai_identifier_str oai:bibliotecadigital.ipb.pt:10198/17547
organization_str_mv urn:organizationAcronym:ipb
person_str_mv Braz-César, Manuel
Braz-César, Manuel
https://www.ciencia-id.pt/5C10-B764-22E3
5C10-B764-22E3
http://orcid.org/0000-0001-5640-0714
0000-0001-5640-0714
Barros, Rui
publishDate 2015
publisher.none.fl_str_mv Universidade da Beira Interior
reponame_str Biblioteca Digital do IPB
repository_id_str urn:repositoryAcronym:ipb
service_str_mv urn:repositoryAcronym:ipb
spelling engUniversidade da Beira Interiorpt_PTFuzzy controllers have been successfully applied to a wide range of engineering problems due its robustness and the ability to deal with non-linear plants. Despite the inherent advantages of these controllers, there is no systematic technique for converting human knowledge into the rule base of a fuzzy inference system. Adaptive neuro-fuzzy inference system (ANFIS) is an artificial intelligence technique that has been successfully used for mapping input-output relationships based on available data sets, i.e., to automatically adjust a fuzzy inference system with a backpropagation algorithm based on training data. This paper presents the application of a ANFIS model to optimize the parameters of a fuzzy controller for structural control of a building structure using a MR damper. The results obtained with the neurofuzzy controller are compared with those of a passive control modes to assess the performance of the proposed control system in reducing the seismic response of the structure.application/pdfpt_PTOptimization of a fuzzy logic controller for MR dampers using ANFISPersonalBraz-César, ManuelDSpacehttp://dspace.org/items/a99f08f6-5b97-4571-a6e4-07dee53fb527DSpacehttp://dspace.org/items/a99f08f6-5b97-4571-a6e4-07dee53fb527Braz-CésarManuelCiência IDhttps://www.ciencia-id.pt5C10-B764-22E3ORCIDhttp://orcid.org0000-0001-5640-0714Scopus Author IDhttps://www.scopus.com53663179600Barros, RuiHostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-989-654-259-72018-05-02T09:52:52Z20152015-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/17547http://purl.org/coar/access_right/c_abf2open accessFuzzy controlSemi-active controlMR dampersStructural dynamicsANFIS1442920 bytesother research producthttp://purl.org/coar/resource_type/c_c94fconference object2015http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/49e1a038-8d0b-4ecf-8d10-f1c30a3a3256/downloadICEUBI2015 - International Conference on Engineering University da Beira Interior “Engineering for Society”Covilhã
spellingShingle Optimization of a fuzzy logic controller for MR dampers using ANFIS
Braz-César, Manuel
Fuzzy control
Semi-active control
MR dampers
Structural dynamics
ANFIS
status SINGLETON
subject.fl_str_mv Fuzzy control
Semi-active control
MR dampers
Structural dynamics
ANFIS
title Optimization of a fuzzy logic controller for MR dampers using ANFIS
title_full Optimization of a fuzzy logic controller for MR dampers using ANFIS
title_fullStr Optimization of a fuzzy logic controller for MR dampers using ANFIS
title_full_unstemmed Optimization of a fuzzy logic controller for MR dampers using ANFIS
title_short Optimization of a fuzzy logic controller for MR dampers using ANFIS
title_sort Optimization of a fuzzy logic controller for MR dampers using ANFIS
topic Fuzzy control
Semi-active control
MR dampers
Structural dynamics
ANFIS
topic_facet Fuzzy control
Semi-active control
MR dampers
Structural dynamics
ANFIS
url http://hdl.handle.net/10198/17547
visible 1