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    Area of Science:

    • Control Theory
    • Artificial Neural Networks
    • Stochastic Systems

    Background:

    • Reaction-diffusion neural networks (SRDNNs) exhibit complex dynamics.
    • Existing sampled-data control (SDC) methods often rely on fixed sampling intervals.
    • Stochastic dynamics in SRDNNs require more precise modeling.

    Purpose of the Study:

    • To develop a multiasynchronous time-space SDC strategy for SRDNNs.
    • To introduce sojourn probabilities (SPs) for accurate stochastic dynamics representation.
    • To design an event-triggered scheme for optimized data transmission.

    Main Methods:

    • Utilizing sojourn probabilities (SPs) instead of transition probabilities.
    • Employing a stochastic variable for aperiodic sampling periods.
    • Developing a time-space SDC strategy with simultaneous temporal and spatial sampling.
    • Incorporating switching gains for enhanced control flexibility.

    Main Results:

    • The proposed SDC strategy effectively controls SRDNNs under stochastic sampling.
    • Sojourn probabilities provide a more accurate representation of SRDNN dynamics.
    • The event-triggered scheme optimizes transmission frequency by addressing aperiodicity.
    • Numerical simulations confirm the efficacy and superiority of the developed control strategy.

    Conclusions:

    • The novel multiasynchronous time-space SDC approach enhances control performance for SRDNNs.
    • The use of SPs and stochastic sampling offers a more robust control framework.
    • The developed strategy demonstrates significant advantages over existing methods in simulations.