Event-triggered extended dissipative synchronization for delayed neural networks with random uncertainties

A. Karnan and G. Nagamani

Chaos, Solitons & Fractals, 2023, vol. 175, issue P1

Abstract: This paper aims to study a generalized robust dynamical behavior called the extended dissipative synchronization of neural networks with time-varying delay and random uncertainties. To achieve the primary objective of minimizing network resource utilization while preserving desired closed-loop performance, an event-triggered control scheme is implemented. By constructing an augmented form of Lyapunov–Krasovskii functional and utilizing generalized integral inequalities, two novel synchronization criteria have been proposed in the form of linear matrix inequalities. It is worth noting that this paper studies a generalized dissipative performance index, enabling the various event-based synchronization problems, including H∞, L2−L∞, passivity, and (Q,S,R)−γ−dissipative synchronization in a unified framework. Ultimately, the efficacy and benefits of the suggested approach are demonstrated through two numerical examples.

Keywords: Delayed neural networks; Random uncertainties; Synchronization; Event-triggered controller; Extended dissipativity performance (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:175:y:2023:i:p1:s0960077923008834

DOI: 10.1016/j.chaos.2023.113982

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