Global quasi-synchronization of complex-valued recurrent neural networks with time-varying delay and interaction terms

Ankit Kumar, Subir Das, Vijay K. Yadav and Rajeev,

Chaos, Solitons & Fractals, 2021, vol. 152, issue C

Abstract: In this article, the global quasi-synchronization of complex-valued recurrent neural networks (CVRNNs) with time-varying delays and interaction terms has been investigated. It is based on the standard Lyapunov stability theory and matrix measure method employed with the nonlinear Lipschitz activation functions. A sufficient condition for global quasi-synchronization of the complex-valued recurrent neural network model is shown in an effective way through a proper description of Lyapunov-stability technique. This article provides quite a new result for the CVRNNs having time-varying delays and interaction terms. Finally, a numerical example is considered to show the viability and unwavering quality of our theoretical results under several conditions.

Keywords: Complex variable; Recurrent neural network; Quasi-synchronization; time-varying delay term; Interaction term; Matrix measure method; Lyapunov stability theory (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921006779

DOI: 10.1016/j.chaos.2021.111323

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