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    Area of Science:

    • Control Systems Engineering
    • Computational Neuroscience
    • Networked Systems

    Background:

    • Delayed neural networks (NNs) present challenges in synchronization due to time-varying delays.
    • Traditional event-triggered schemes (ETS) can lead to redundant data transmission.
    • Effective synchronization is crucial for various applications, including chaotic systems.

    Purpose of the Study:

    • To introduce a novel discrete event-triggered scheme (DETS) for synchronizing delayed neural networks (NNs).
    • To design a dynamic output-feedback controller (DOFC) for achieving robust NN synchronization.
    • To minimize data transmission through an efficient dual-channel setup.

    Main Methods:

    • Development of a discrete event-triggered scheme (DETS) utilizing both current and past samples.
    • Design of a dynamic output-feedback controller (DOFC) for synchronization.
    • Application of Lyapunov-Krasovskii functional and Cone-complementarity linearization (CCL) for stability analysis and parameter co-design.

    Main Results:

    • The proposed DETS effectively synchronizes delayed neural networks.
    • The dual-channel setup significantly reduces redundant data transmission.
    • Stability criteria for the synchronization error system were successfully derived.

    Conclusions:

    • The novel DETS offers an efficient approach for synchronizing delayed neural networks.
    • The co-design methodology ensures robust controller and trigger parameter selection.
    • The method demonstrates practical effectiveness, as shown in a chaotic system example.