https://doi.org/10.1140/epjs/s11734-024-01343-8
Regular Article
Analysis of synchronization problems in delayed neural networks with Markovian jumping via looped functionals
1
Department of Mathematics, Bannari Amman Institute of Technology, 638401, Erode, Tamil Nadu, India
2
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, 600127, Chennai, Tamil Nadu, India
Received:
15
May
2024
Accepted:
7
September
2024
Published online:
4
October
2024
This paper focuses on the sampled-data-based synchronization criteria for delayed neural networks (DNNs) with Markovian jumping parameters. To derive synchronization criteria, sampled-data control is considered in the response DNNs with Markovian jumping parameters. An improved looped Lyapunov functional is proposed to derive sufficient conditions for ensuring the error DNNs is mean square stable. The looped Lyapunov functional is constructed which has information about sampling instants. A matrix-separation-based inequality is used to obtain less conservatism. Under sufficient conditions, the control gain matrices synchronize the drive and response DNNs with each other. Finally, numerical example and simulation results are provided to demonstrate the superiority of the investigations.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
