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Stability and Synchronization of Switched Multi-Rate Recurrent Neural Networks

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dc.contributor.authorRuiz, Victoria
dc.contributor.authorAranda Escolástico, Ernesto
dc.contributor.authorSalt, Julián
dc.contributor.authorDormido Canto, Sebastián
dc.contributor.orcidhttps://orcid.org/0000-0003-2993-7705
dc.contributor.orcidhttps://orcid.org/0000-0002-9640-2658
dc.date.accessioned2024-10-08T10:21:46Z
dc.date.available2024-10-08T10:21:46Z
dc.date.issued2021
dc.descriptionThe registered version of this article, first published in “IEEE Access, vol. 9", is available online at the publisher's website: IEEE, https://doi.org/10.1109/ACCESS.2021.3067452 La versión registrada de este artículo, publicado por primera vez en “IEEE Access, vol. 9", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/ACCESS.2021.3067452
dc.description.abstractSeveral designs of recurrent neural networks have been proposed in the literature involving different clock times. However, the stability and synchronization of this kind of system have not been studied. In this paper, we consider that each neuron or group of neurons of a switched recurrent neural network can have a different sampling period for its activation, which we call switched multi-rate recurrent neural networks, and we propose a dynamical model to describe it. Through Lyapunov methods, sufficient conditions are provided to guarantee the exponential stability of the network. Additionally, these results are extended to the synchronization problem of two identical networks, understanding the synchronization as the agreement of both of them in time. Numerical simulations are presented to validate the theoretical results. The proposed method might help to design more efficient and less computationally demanding neural networks.en
dc.description.versionversión final
dc.identifier.citationV. Ruiz, E. Aranda-Escolástico, J. Salt and S. Dormido, "Stability and Synchronization of Switched Multi-Rate Recurrent Neural Networks," in IEEE Access, vol. 9, pp. 45614-45621, 2021, doi: 10.1109/ACCESS.2021.3067452.
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2021.3067452
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23954
dc.journal.titleIEEE Access
dc.journal.volume9
dc.language.isoen
dc.page.final45621
dc.page.initial45614
dc.publisherIEEE
dc.relation.centerFacultades y escuelas
dc.relation.departmentIngeniería de Software y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsrecurrent neural networksen
dc.subject.keywordsmulti-rate systemsen
dc.subject.keywordsswitched systemsen
dc.subject.keywordsLyapunov methodsen
dc.titleStability and Synchronization of Switched Multi-Rate Recurrent Neural Networksen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication19c0c538-4e7e-4de5-afd9-6ff1a8bbf88e
relation.isAuthorOfPublicationf5f57d8a-f3c0-40a1-a93c-80d6237a2bcb
relation.isAuthorOfPublication.latestForDiscovery19c0c538-4e7e-4de5-afd9-6ff1a8bbf88e
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