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https://hdl.handle.net/1822/47159
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Campo DC | Valor | Idioma |
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dc.contributor.author | Oliveira, José J. | por |
dc.date.accessioned | 2017-11-09T09:26:55Z | - |
dc.date.issued | 2017-12 | - |
dc.date.submitted | 2017-01-30 | - |
dc.identifier.issn | 0893-6080 | por |
dc.identifier.uri | https://hdl.handle.net/1822/47159 | - |
dc.description.abstract | 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. | por |
dc.description.sponsorship | 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. | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147370/PT | por |
dc.rights | openAccess | - |
dc.subject | Cohen-Grossberg neural networks | por |
dc.subject | Infinite distributed delays | por |
dc.subject | Infinite discrete delays | por |
dc.subject | Global exponential stability | por |
dc.subject | Unbounded coefficients | por |
dc.title | Global exponential stability of nonautonomous neural network models with unbounded delays | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0893608017302083 | por |
oaire.citationStartPage | 71 | por |
oaire.citationEndPage | 79 | por |
oaire.citationVolume | 96 | por |
dc.identifier.doi | 10.1016/j.neunet.2017.09.006 | por |
dc.identifier.pmid | 28987978 | por |
dc.subject.fos | Ciências Naturais::Matemáticas | por |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Neural Networks | por |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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manuscript.pdf | 373,73 kB | Adobe PDF | Ver/Abrir |