Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms

Detalhes bibliográficos
Autor(a) principal: Machado, J. A. Tenreiro
Data de Publicação: 2010
Outros Autores: Galhano, Alexandra, Oliveira, Anabela, Tar, József K.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10400.22/4276
Resumo: This study addresses the optimization of rational fraction approximations for the discrete-time calculation of fractional derivatives. The article starts by analyzing the standard techniques based on Taylor series and Padé expansions. In a second phase the paper re-evaluates the problem in an optimization perspective by tacking advantage of the flexibility of the genetic algorithms.
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spelling Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithmsFractional derivativesFractional calculusOptimizationGenetic algorithmsThis study addresses the optimization of rational fraction approximations for the discrete-time calculation of fractional derivatives. The article starts by analyzing the standard techniques based on Taylor series and Padé expansions. In a second phase the paper re-evaluates the problem in an optimization perspective by tacking advantage of the flexibility of the genetic algorithms.ElsevierREPOSITÓRIO P.PORTOMachado, J. A. TenreiroGalhano, AlexandraOliveira, AnabelaTar, József K.2014-03-28T11:30:32Z20102010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/4276eng1007-570410.1016/j.cnsns.2009.04.030info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-04-02T03:14:55Zoai:recipp.ipp.pt:10400.22/4276Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T00:48:00.815939Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms
title Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms
spellingShingle Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms
Machado, J. A. Tenreiro
Fractional derivatives
Fractional calculus
Optimization
Genetic algorithms
title_short Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms
title_full Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms
title_fullStr Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms
title_full_unstemmed Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms
title_sort Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms
author Machado, J. A. Tenreiro
author_facet Machado, J. A. Tenreiro
Galhano, Alexandra
Oliveira, Anabela
Tar, József K.
author_role author
author2 Galhano, Alexandra
Oliveira, Anabela
Tar, József K.
author2_role author
author
author
dc.contributor.none.fl_str_mv REPOSITÓRIO P.PORTO
dc.contributor.author.fl_str_mv Machado, J. A. Tenreiro
Galhano, Alexandra
Oliveira, Anabela
Tar, József K.
dc.subject.por.fl_str_mv Fractional derivatives
Fractional calculus
Optimization
Genetic algorithms
topic Fractional derivatives
Fractional calculus
Optimization
Genetic algorithms
description This study addresses the optimization of rational fraction approximations for the discrete-time calculation of fractional derivatives. The article starts by analyzing the standard techniques based on Taylor series and Padé expansions. In a second phase the paper re-evaluates the problem in an optimization perspective by tacking advantage of the flexibility of the genetic algorithms.
publishDate 2010
dc.date.none.fl_str_mv 2010
2010-01-01T00:00:00Z
2014-03-28T11:30:32Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/4276
url http://hdl.handle.net/10400.22/4276
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1007-5704
10.1016/j.cnsns.2009.04.030
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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repository.mail.fl_str_mv info@rcaap.pt
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