Continuous-time additive Hopfield-type neural networks with impulses

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dc.contributor.authorAkca, Haydar
dc.contributor.authorAlassar, Rajai
dc.contributor.authorCovachev, Valéry
dc.contributor.authorETAL..
dc.date.accessioned2022-01-05T12:12:07Z
dc.date.accessioned2023-08-19T09:16:15Z
dc.date.available2022-01-05T12:12:07Z
dc.date.available2023-08-19T09:16:15Z
dc.date.issued2003-01
dc.descriptionAnyone can see that the human brain is superior to a digital computer at many tasks. For example, from the processing of visual information point of view; a one-year-old baby is much better and faster at recognizing objects, faces, and so on than even the most advanced fastest supercomputer systemsen_US
dc.description.abstractWe investigate the global stability characteristics of a system of equations modelling the dynamics of additive Hopfield-type neural networks with impulses in the continuous-time case.en_US
dc.identifier.citationAkca, H., Alassar, R., Covachev, V., Covacheva, Z., & Al-Zahrani, E. (2004). Continuous-time additive Hopfield-type neural networks with impulses. Journal of Mathematical Analysis and Applications, 290(2), 436-451.en_US
dc.identifier.urihttps://edms.wexl.in/handle/1/2124
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectImpulsive equationsen_US
dc.subjectAdditive Hopfield-type neural networksen_US
dc.subjectGlobal stabilityen_US
dc.titleContinuous-time additive Hopfield-type neural networks with impulsesen_US
dc.title.alternativeJournal of Mathematical Analysis and Applications, 290(2), 436-451en_US
dc.typeArticleen_US

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