Global exponential stability of discrete-time Hopfield neural network models with unbounded delays
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Texto Completo: | https://hdl.handle.net/1822/78376 |
Resumo: | In this paper, a general setting is presented to study the exponential stability of discrete-time systems with bounded or unbounded delays. Based on the M-matrix theory, we establish sufficient conditions to ensure the global exponential stability of the zero equilibrium of low-order, and high-order, discrete-time Hopfield neural network models with unbounded delays and delay in the leakage terms. A comparison of the literature shows that our results generalize and improve some in recent publications. |
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Global exponential stability of discrete-time Hopfield neural network models with unbounded delaysNeural networksDelay difference equationsUnbounded delaysGlobal stabilityCiências Naturais::MatemáticasScience & TechnologyIn this paper, a general setting is presented to study the exponential stability of discrete-time systems with bounded or unbounded delays. Based on the M-matrix theory, we establish sufficient conditions to ensure the global exponential stability of the zero equilibrium of low-order, and high-order, discrete-time Hopfield neural network models with unbounded delays and delay in the leakage terms. A comparison of the literature shows that our results generalize and improve some in recent publications.Fundação para a Ciência e Tecnologia (FCT) UIDB/00013/2020 and UIDP/00013/2020Taylor & FrancisUniversidade do MinhoOliveira, José J.2022-05-162022-05-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/78376eng1023-61981563-512010.1080/10236198.2022.2073820https://www.tandfonline.com/doi/full/10.1080/10236198.2022.2073820info: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:RCAAP2024-05-11T04:10:26Zoai:repositorium.sdum.uminho.pt:1822/78376Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:41:13.133118Repositórios 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 |
Global exponential stability of discrete-time Hopfield neural network models with unbounded delays |
| title |
Global exponential stability of discrete-time Hopfield neural network models with unbounded delays |
| spellingShingle |
Global exponential stability of discrete-time Hopfield neural network models with unbounded delays Oliveira, José J. Neural networks Delay difference equations Unbounded delays Global stability Ciências Naturais::Matemáticas Science & Technology |
| title_short |
Global exponential stability of discrete-time Hopfield neural network models with unbounded delays |
| title_full |
Global exponential stability of discrete-time Hopfield neural network models with unbounded delays |
| title_fullStr |
Global exponential stability of discrete-time Hopfield neural network models with unbounded delays |
| title_full_unstemmed |
Global exponential stability of discrete-time Hopfield neural network models with unbounded delays |
| title_sort |
Global exponential stability of discrete-time Hopfield 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 |
Neural networks Delay difference equations Unbounded delays Global stability Ciências Naturais::Matemáticas Science & Technology |
| topic |
Neural networks Delay difference equations Unbounded delays Global stability Ciências Naturais::Matemáticas Science & Technology |
| description |
In this paper, a general setting is presented to study the exponential stability of discrete-time systems with bounded or unbounded delays. Based on the M-matrix theory, we establish sufficient conditions to ensure the global exponential stability of the zero equilibrium of low-order, and high-order, discrete-time Hopfield neural network models with unbounded delays and delay in the leakage terms. A comparison of the literature shows that our results generalize and improve some in recent publications. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-05-16 2022-05-16T00:00:00Z |
| 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 |
https://hdl.handle.net/1822/78376 |
| url |
https://hdl.handle.net/1822/78376 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
1023-6198 1563-5120 10.1080/10236198.2022.2073820 https://www.tandfonline.com/doi/full/10.1080/10236198.2022.2073820 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
| dc.publisher.none.fl_str_mv |
Taylor & Francis |
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Taylor & Francis |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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