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Exponential Synchronization of Networked Chaotic Delayed Neural Network by a Hybrid Event Trigger Scheme.

Zhongyang Fei, Chaoxu Guan, Huijun Gao

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    This study achieves exponential synchronization for chaotic delayed neural networks using a hybrid event-triggered control. This approach conserves network resources while ensuring system stability and performance.

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    Area of Science:

    • Chaos theory
    • Neural networks
    • Control systems

    Background:

    • Master-slave chaotic delayed neural networks are complex systems.
    • Network control introduces challenges like external disturbances and delays.
    • Limited communication capacity and bandwidth necessitate efficient control strategies.

    Purpose of the Study:

    • To achieve exponential synchronization in master-slave chaotic delayed neural networks.
    • To develop an event-triggered control scheme that conserves network resources.
    • To address external disturbances and network-induced delays within a network control framework.

    Main Methods:

    • A hybrid event-triggered control scheme is proposed.
    • Lyapunov functional is utilized to derive stability criteria.
    • The error system is analyzed for (κ, μ, θ)-dissipativity performance.
    • Co-design of the event-trigger scheme and controller is performed.

    Main Results:

    • A sufficient criterion for exponential synchronization is established.
    • The proposed hybrid event-triggered control reduces data transmission.
    • The Zeno phenomenon is avoided.
    • The method ensures stability and synchronization under network constraints.

    Conclusions:

    • The developed hybrid event-triggered control scheme effectively achieves exponential synchronization.
    • The approach conserves network resources and avoids the Zeno phenomenon.
    • The results are validated through a numerical example, demonstrating practical applicability.