Anti-synchronization analysis of chaotic neural networks using delay product type looped-Lyapunov functional

Bhuvaneshwari Ganesan and Manivannan Annamalai

Chaos, Solitons & Fractals, 2023, vol. 174, issue C

Abstract: This article examines the anti-synchronization (A-S) problem of time-varying delayed chaotic neural networks (NNs). A memory non-fragile sampled data controller (MNFSDC) has been constructed to effectively transmit information over networks, in which the control gain matrices include uncertainty. A new delay-product type looped Lyapunov functional has been introduced that includes the delay terms (h2−h(t)) and h(t) with sampling instant informations. The less conservative results are obtained by utilizing integral inequalities. The asymptotic stability of the error system is ensured by deriving sufficient conditions in the form of a linear matrix inequality. The proposed MNFSDC scheme anti-synchronizes the master and slave systems. Furthermore, numerical simulations are provided to validate the proposed result ensuring the A-S nature of the proposed chaotic NNs. Besides, comparison study is also given to show the proposed results are more efficient than the existing works.

Keywords: Anti-synchronization; Chaotic neural networks; Memory non-fragile sampled-data control; Delay-product looped Lyapunov functional; Time-delay (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007993

DOI: 10.1016/j.chaos.2023.113898

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