Regional sampled-data synchronization of chaotic neural networks using piecewise-continuous delay dependent Lyapunov functional

S.Y. Han, S.K. Kommuri, O.M. Kwon and S.M. Lee

Applied Mathematics and Computation, 2022, vol. 423, issue C

Abstract: In this paper, a regional sampled-data synchronization criterion is proposed for the chaotic neural networks (CNNs) with input saturation using the piecewise-continuous delay dependent Lyapunov functional (PDDLF) approach. The aim of this work is to enlarge the region of attraction (ROA) of the synchronous state for CNNs with input saturation. Unlike existing works, the Lyapunov functional in the proposed approach is constructed from a polynomial with respect to the piecewise-continuous delay. Moreover, the proposed Lyapunov functional is combined with looped-functionals to derive the sufficient condition. The synchronization criterion is formulated in terms of sum of squares (SOS) programs, which reduces the infinite-dimensional linear matrix inequality (LMI) conditions to a finite number of SOS conditions. A numerical example is presented to illustrate the effectiveness and advantages of the proposed approach.

Keywords: Chaotic neural networks; Synchronization; Piecewise-continuous delay dependent Lyapunov functional; Sum of squares (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:423:y:2022:i:c:s0096300322000807

DOI: 10.1016/j.amc.2022.126994

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