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Mode-Mixed Effects Based Intralayer-Dependent Impulsive Synchronization for Multiple Mismatched Multilayer Neural

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    This study achieves impulsive synchronization in complex multilayer neural networks (NNs) with mismatched parameters and mode-mixed effects. Novel control strategies ensure reliable synchronization for secure communication applications.

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    Area of Science:

    • Complex Systems
    • Computational Neuroscience
    • Control Theory

    Background:

    • Existing multilayer neural network (NN) models often impose restrictive one-to-one interlayer coupling constraints.
    • Diverse real-world applications require more flexible NN models with nonidentical parameters and mismatched connections.
    • Synchronization of NNs with time delays and complex mode-mixed effects presents significant analytical challenges.

    Purpose of the Study:

    • To establish a novel multilayer NN model that relaxes interlayer coupling constraints and accommodates nonidentical parameters.
    • To design a hybrid control strategy for achieving synchronization in mismatched multilayer NNs with time delays.
    • To incorporate and analyze mode-mixed effects arising from intralayer coupling delays and switched topologies.

    Main Methods:

    • Development of a novel multilayer neural network model without one-to-one interlayer coupling.
    • Design of a hybrid controller combining intralayer-dependent impulsive control and switched feedback control.
    • Creation of a new analysis framework utilizing super-Laplacian matrix, augmented matrix, and mode-mixed methods.

    Main Results:

    • Successful intralayer-dependent impulsive synchronization of multiple mismatched multilayer neural networks (NNs).
    • Effective handling of mode-mixed effects caused by intralayer coupling delays and switched topologies.
    • Validation of synchronization results through numerical simulations demonstrating potential for secure communication.

    Conclusions:

    • The proposed novel multilayer NN model and control strategy effectively achieve synchronization under complex conditions.
    • The developed analysis framework accurately addresses mode-mixed effects and intralayer coupling dynamics.
    • The findings offer a robust approach for synchronization in advanced neural network systems, applicable to secure communication.