Fixed-Time Synchronization of Chaotic Neural Networks with Mixed Delays via Dynamic Event-Triggered Control

Abstract

This paper proposes a novel dynamic event-triggered control scheme to address the fixed-time synchronization problem for chaotic neural networks (NNs) with mixed delays. Firstly, an adaptive threshold mechanism is embedded into the dynamic event-triggered control, which occupies less communication resource in comparison with the periodic-triggered control and enables the exclusion of Zeno phenomena. Secondly, by combining Lyapunov stability theory with fixed-time convergence criteria, a sufficient condition for the fixed-time synchronization of such chaotic NNs is established. Particularly, an explicit upper-bound estimation of the settling time is derived, which solely depends on controller parameters and is independent of the initial condition. Theoretical analysis indicates that the error system can converge to a predefined neighborhood of the origin within a fixed time. Finally, numerical simulations further substantiate the feasibility and superiority of the proposed methods.OPEN ACCESS Received: 02/09/2025 Accepted: 22/12/2025 Published: 16/04/2026

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