Hybrid event-triggered synchronization control of delayed chaotic neural networks against communication delay and random data loss

Zifan Gao, Dawei Zhang and Shuqian Zhu

Chaos, Solitons & Fractals, 2023, vol. 172, issue C

Abstract: This paper investigates the network-based synchronization control of delayed chaotic neural networks. A new hybrid event-triggered communication scheme, which consists of fixed and dynamic triggering conditions, is presented to handle the random loss of triggered sampled-data. Under this scheme, a quantitative relation between the allowable data loss probability and the fixed triggering interval can be revealed. The synchronous error system is modeled by a multi-constrained system affected by nonlinear terms, piecewise input delay, stochastic update input and reset states. By using the boundary information and interrelation of both communication delay and input delay, a binary quadratic convex lemma and a tailored discontinuous augmented Lyapunov-Krasovskii functional approach are proposed to exploit delay-dependent synchronization criteria with less conservatism. Simulation results are provided for checking the merits of the obtained criteria.

Keywords: Chaotic synchronization; Delayed chaotic neural networks; Hybrid event-triggered communication; Random data loss; Binary quadratic convex lemma (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:172:y:2023:i:c:s0960077923004368

DOI: 10.1016/j.chaos.2023.113535

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