Synchronization of Caputo fractional neural networks with bounded time variable delays

Detalhes bibliográficos
Autor(a) principal: Almeida, Ricardo
Data de Publicação: 2021
Outros Autores: Hristova, Snezhana, Tersian, Stepan
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/32629
Resumo: One of the main problems connected with neural networks is synchronization. We examine a model of a neural network with time-varying delay and also the case when the connection weights (the influential strength of the jth neuron to the ith neuron) are variable in time and unbounded. The rate of change of the dynamics of all neurons is described by the Caputo fractional derivative. We apply Lyapunov functions and the Razumikhin method to obtain some sufficient conditions to ensure synchronization in the model. These sufficient conditions are explicitly expressed in terms of the parameters of the system, and hence, they are easily verifiable. We illustrate our theory with a particular nonlinear neural network.
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spelling Synchronization of Caputo fractional neural networks with bounded time variable delaysNonlinear neural networksDelaySynchronizationLyapunov functionsOne of the main problems connected with neural networks is synchronization. We examine a model of a neural network with time-varying delay and also the case when the connection weights (the influential strength of the jth neuron to the ith neuron) are variable in time and unbounded. The rate of change of the dynamics of all neurons is described by the Caputo fractional derivative. We apply Lyapunov functions and the Razumikhin method to obtain some sufficient conditions to ensure synchronization in the model. These sufficient conditions are explicitly expressed in terms of the parameters of the system, and hence, they are easily verifiable. We illustrate our theory with a particular nonlinear neural network.De Gruyter Open2021-11-23T10:32:01Z2021-01-01T00:00:00Z2021-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/32629eng2391-545510.1515/math-2021-0046Almeida, RicardoHristova, SnezhanaTersian, Stepaninfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:02:41Zoai:ria.ua.pt:10773/32629Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:04:10.112689Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Synchronization of Caputo fractional neural networks with bounded time variable delays
title Synchronization of Caputo fractional neural networks with bounded time variable delays
spellingShingle Synchronization of Caputo fractional neural networks with bounded time variable delays
Almeida, Ricardo
Nonlinear neural networks
Delay
Synchronization
Lyapunov functions
title_short Synchronization of Caputo fractional neural networks with bounded time variable delays
title_full Synchronization of Caputo fractional neural networks with bounded time variable delays
title_fullStr Synchronization of Caputo fractional neural networks with bounded time variable delays
title_full_unstemmed Synchronization of Caputo fractional neural networks with bounded time variable delays
title_sort Synchronization of Caputo fractional neural networks with bounded time variable delays
author Almeida, Ricardo
author_facet Almeida, Ricardo
Hristova, Snezhana
Tersian, Stepan
author_role author
author2 Hristova, Snezhana
Tersian, Stepan
author2_role author
author
dc.contributor.author.fl_str_mv Almeida, Ricardo
Hristova, Snezhana
Tersian, Stepan
dc.subject.por.fl_str_mv Nonlinear neural networks
Delay
Synchronization
Lyapunov functions
topic Nonlinear neural networks
Delay
Synchronization
Lyapunov functions
description One of the main problems connected with neural networks is synchronization. We examine a model of a neural network with time-varying delay and also the case when the connection weights (the influential strength of the jth neuron to the ith neuron) are variable in time and unbounded. The rate of change of the dynamics of all neurons is described by the Caputo fractional derivative. We apply Lyapunov functions and the Razumikhin method to obtain some sufficient conditions to ensure synchronization in the model. These sufficient conditions are explicitly expressed in terms of the parameters of the system, and hence, they are easily verifiable. We illustrate our theory with a particular nonlinear neural network.
publishDate 2021
dc.date.none.fl_str_mv 2021-11-23T10:32:01Z
2021-01-01T00:00:00Z
2021-01
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/10773/32629
url http://hdl.handle.net/10773/32629
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2391-5455
10.1515/math-2021-0046
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 De Gruyter Open
publisher.none.fl_str_mv De Gruyter Open
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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