Global exponential stability of nonautonomous neural network models with unbounded delays
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/47159 |
Resumo: | For a nonautonomous class of n-dimensional di erential system with in nite delays, we give su cient conditions for its global exponential stability, without showing the existence of an equilibrium point, or a periodic solution, or an almost periodic solution. We apply our main result to several concrete neural network models, studied in the literature, and a comparison of results is given. Contrary to usual in the literature about neural networks, the assumption of bounded coe cients is not need to obtain the global exponential stability. Finally, we present numerical examples to illustrate the e ectiveness of our results. |
id |
RCAP_ef8b75b9e730c7cde890387e5b794e99 |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/47159 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Global exponential stability of nonautonomous neural network models with unbounded delaysCohen-Grossberg neural networksInfinite distributed delaysInfinite discrete delaysGlobal exponential stabilityUnbounded coefficientsCiências Naturais::MatemáticasScience & TechnologyFor a nonautonomous class of n-dimensional di erential system with in nite delays, we give su cient conditions for its global exponential stability, without showing the existence of an equilibrium point, or a periodic solution, or an almost periodic solution. We apply our main result to several concrete neural network models, studied in the literature, and a comparison of results is given. Contrary to usual in the literature about neural networks, the assumption of bounded coe cients is not need to obtain the global exponential stability. Finally, we present numerical examples to illustrate the e ectiveness of our results.The paper was supported by the Research Center of Mathematics of University of Minho with the Portuguese Funds from the FCT - “Fundação para a Ciência e a Tecnologia”, through the Project UID/MAT/00013/2013. The author thanks the referees for valuable comments.info:eu-repo/semantics/publishedVersionElsevierUniversidade do MinhoOliveira, José J.2017-122017-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/47159eng0893-608010.1016/j.neunet.2017.09.00628987978https://www.sciencedirect.com/science/article/pii/S0893608017302083info: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:RCAAP2023-07-21T12:14:12Zoai:repositorium.sdum.uminho.pt:1822/47159Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:06:26.841793Repositório 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 |
Global exponential stability of nonautonomous neural network models with unbounded delays |
title |
Global exponential stability of nonautonomous neural network models with unbounded delays |
spellingShingle |
Global exponential stability of nonautonomous neural network models with unbounded delays Oliveira, José J. Cohen-Grossberg neural networks Infinite distributed delays Infinite discrete delays Global exponential stability Unbounded coefficients Ciências Naturais::Matemáticas Science & Technology |
title_short |
Global exponential stability of nonautonomous neural network models with unbounded delays |
title_full |
Global exponential stability of nonautonomous neural network models with unbounded delays |
title_fullStr |
Global exponential stability of nonautonomous neural network models with unbounded delays |
title_full_unstemmed |
Global exponential stability of nonautonomous neural network models with unbounded delays |
title_sort |
Global exponential stability of nonautonomous neural network models with unbounded delays |
author |
Oliveira, José J. |
author_facet |
Oliveira, José J. |
author_role |
author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Oliveira, José J. |
dc.subject.por.fl_str_mv |
Cohen-Grossberg neural networks Infinite distributed delays Infinite discrete delays Global exponential stability Unbounded coefficients Ciências Naturais::Matemáticas Science & Technology |
topic |
Cohen-Grossberg neural networks Infinite distributed delays Infinite discrete delays Global exponential stability Unbounded coefficients Ciências Naturais::Matemáticas Science & Technology |
description |
For a nonautonomous class of n-dimensional di erential system with in nite delays, we give su cient conditions for its global exponential stability, without showing the existence of an equilibrium point, or a periodic solution, or an almost periodic solution. We apply our main result to several concrete neural network models, studied in the literature, and a comparison of results is given. Contrary to usual in the literature about neural networks, the assumption of bounded coe cients is not need to obtain the global exponential stability. Finally, we present numerical examples to illustrate the e ectiveness of our results. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12 2017-12-01T00:00:00Z |
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/1822/47159 |
url |
http://hdl.handle.net/1822/47159 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0893-6080 10.1016/j.neunet.2017.09.006 28987978 https://www.sciencedirect.com/science/article/pii/S0893608017302083 |
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1799132480381386752 |