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    Area of Science:

    • Control Systems Engineering
    • Computational Neuroscience
    • Networked Systems

    Background:

    • Neural networks are susceptible to performance degradation due to mixed delays (discrete and distributed).
    • Event-triggered control strategies are crucial for reducing communication overhead in networked systems.
    • Existing methods may not efficiently handle the complexities of mixed delays and communication burdens.

    Purpose of the Study:

    • To develop a memory-event-triggered H∞ output feedback control strategy for neural networks with mixed delays.
    • To minimize network communication load by selectively transmitting data.
    • To ensure robust stability and performance of the controlled neural network system.

    Main Methods:

    • A novel memory-event-triggered scheme (METS) utilizing historical system outputs to determine data transmission.
    • Modeling communication delay probability density as the kernel of a distributed delay.
    • Construction of a Lyapunov-Krasovskii functional (LKF) incorporating the distributed delay kernel and generalized integral inequality.
    • Derivation of sufficient conditions for controller design using linear matrix inequalities (LMIs).

    Main Results:

    • Sufficient conditions for designing an event-triggered H∞ controller were successfully derived using LMIs.
    • The proposed METS effectively reduces data transmission compared to traditional periodic triggering.
    • Experimental validation on both a computer simulation and a real wireless network confirmed the method's efficacy.

    Conclusions:

    • The developed memory-event-triggered H∞ control method is effective for neural networks with mixed delays.
    • The METS significantly alleviates network communication burden while maintaining control performance.
    • The findings provide a practical approach for implementing efficient control in real-world networked systems.