https://doi.org/10.1140/epjs/s11734-024-01300-5
Regular Article
Synchronization of fractional order time delayed neural networks using matrix measure approach
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology - Chennai, 600127, Chennai, Tamilnadu, India
Received:
2
April
2024
Accepted:
7
August
2024
Published online:
23
August
2024
This research delves into the utilization of the matrix measure approach (MMA) for the synchronization of fractional order neural networks (FONNs) incorporating time delays. This study introduces a set of criteria for achieving control input within the slave system (FONNs), employing a novel approach based on fractional order Dini-like derivatives within the matrix measure framework. The proposed criteria formulated in fractional order, encompass diverse conditions that align with several sufficient conditions inherent in MMA. Synchronization between the slave and master system is established, ensuring the asymptotic stability between them. Finally, by presenting the numerical result, the efficacy of FONNs synchronization is obtained.
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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.
