Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model

Detalhes bibliográficos
Autor(a) principal: BERTONE,A.M.A.
Data de Publicação: 2017
Outros Autores: MARTINS,J.B., YAMANAKA,K.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512017000300405
Resumo: ABSTRACT A fuzzy identification of the system’s dynamic is developed with a data generated by a hydrogen fuel cell simulator. The data obtained is single input/single output, without having previous knowledge of the system model, and showing nonlinear behavior. The choice of the fuzzy method for identification is based on those particular data features, and the malleability of the mathematical fuzzy technique. The objective of the fuzzy identification is to reach an analytic formula for a better understanding of the cause-effect relationships of the data, followed by its validation. The dynamic system identification process is performed using fuzzy clustering through the Gustafson and Kessel algorithm, followed by a Takagi and Sugeno fuzzy inference method. The k-fold technique, is the cross validation tool, used to confirm the lack of data over-training. The novelty of this approach covers mathematical and engineering features that makes this study interdisciplinary. For the mathematical contribution, there is a three-dimensional graphic interpretation of the data clustering geometry, obtained through own code computer simulations. Concerning to the engineering context, the novelty is based on the use of the fuzzy approach to the hydrogen fuel cell. Both contributions have no precedent in the literature. The results of the fuzzy identification show high reliability in terms of cross validation, making the fuzzy approach a promising tool for black-box identification. Combining this technique with others will provide powerful instrument for industrial problems.
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spelling Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Modelhydrogen fuel cellfuzzy clusteringidentification of dynamical systemsTakagi Sugeno inference methodABSTRACT A fuzzy identification of the system’s dynamic is developed with a data generated by a hydrogen fuel cell simulator. The data obtained is single input/single output, without having previous knowledge of the system model, and showing nonlinear behavior. The choice of the fuzzy method for identification is based on those particular data features, and the malleability of the mathematical fuzzy technique. The objective of the fuzzy identification is to reach an analytic formula for a better understanding of the cause-effect relationships of the data, followed by its validation. The dynamic system identification process is performed using fuzzy clustering through the Gustafson and Kessel algorithm, followed by a Takagi and Sugeno fuzzy inference method. The k-fold technique, is the cross validation tool, used to confirm the lack of data over-training. The novelty of this approach covers mathematical and engineering features that makes this study interdisciplinary. For the mathematical contribution, there is a three-dimensional graphic interpretation of the data clustering geometry, obtained through own code computer simulations. Concerning to the engineering context, the novelty is based on the use of the fuzzy approach to the hydrogen fuel cell. Both contributions have no precedent in the literature. The results of the fuzzy identification show high reliability in terms of cross validation, making the fuzzy approach a promising tool for black-box identification. Combining this technique with others will provide powerful instrument for industrial problems.Sociedade Brasileira de Matemática Aplicada e Computacional2017-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512017000300405TEMA (São Carlos) v.18 n.3 2017reponame:TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)instname:Sociedade Brasileira de Matemática Aplicada e Computacionalinstacron:SBMAC10.5540/tema.2017.018.03.0405info:eu-repo/semantics/openAccessBERTONE,A.M.A.MARTINS,J.B.YAMANAKA,K.eng2018-02-08T00:00:00Zoai:scielo:S2179-84512017000300405Revistahttp://www.scielo.br/temaPUBhttps://old.scielo.br/oai/scielo-oai.phpcastelo@icmc.usp.br2179-84511677-1966opendoar:2018-02-08T00:00TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) - Sociedade Brasileira de Matemática Aplicada e Computacionalfalse
dc.title.none.fl_str_mv Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model
title Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model
spellingShingle Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model
BERTONE,A.M.A.
hydrogen fuel cell
fuzzy clustering
identification of dynamical systems
Takagi Sugeno inference method
title_short Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model
title_full Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model
title_fullStr Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model
title_full_unstemmed Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model
title_sort Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model
author BERTONE,A.M.A.
author_facet BERTONE,A.M.A.
MARTINS,J.B.
YAMANAKA,K.
author_role author
author2 MARTINS,J.B.
YAMANAKA,K.
author2_role author
author
dc.contributor.author.fl_str_mv BERTONE,A.M.A.
MARTINS,J.B.
YAMANAKA,K.
dc.subject.por.fl_str_mv hydrogen fuel cell
fuzzy clustering
identification of dynamical systems
Takagi Sugeno inference method
topic hydrogen fuel cell
fuzzy clustering
identification of dynamical systems
Takagi Sugeno inference method
description ABSTRACT A fuzzy identification of the system’s dynamic is developed with a data generated by a hydrogen fuel cell simulator. The data obtained is single input/single output, without having previous knowledge of the system model, and showing nonlinear behavior. The choice of the fuzzy method for identification is based on those particular data features, and the malleability of the mathematical fuzzy technique. The objective of the fuzzy identification is to reach an analytic formula for a better understanding of the cause-effect relationships of the data, followed by its validation. The dynamic system identification process is performed using fuzzy clustering through the Gustafson and Kessel algorithm, followed by a Takagi and Sugeno fuzzy inference method. The k-fold technique, is the cross validation tool, used to confirm the lack of data over-training. The novelty of this approach covers mathematical and engineering features that makes this study interdisciplinary. For the mathematical contribution, there is a three-dimensional graphic interpretation of the data clustering geometry, obtained through own code computer simulations. Concerning to the engineering context, the novelty is based on the use of the fuzzy approach to the hydrogen fuel cell. Both contributions have no precedent in the literature. The results of the fuzzy identification show high reliability in terms of cross validation, making the fuzzy approach a promising tool for black-box identification. Combining this technique with others will provide powerful instrument for industrial problems.
publishDate 2017
dc.date.none.fl_str_mv 2017-12-01
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=S2179-84512017000300405
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.5540/tema.2017.018.03.0405
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 Matemática Aplicada e Computacional
publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
dc.source.none.fl_str_mv TEMA (São Carlos) v.18 n.3 2017
reponame:TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
instname:Sociedade Brasileira de Matemática Aplicada e Computacional
instacron:SBMAC
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reponame_str TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
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repository.name.fl_str_mv TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) - Sociedade Brasileira de Matemática Aplicada e Computacional
repository.mail.fl_str_mv castelo@icmc.usp.br
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