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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Computational Neuroscience

    Background:

    • Delayed recurrent neural networks (DRNNs) are crucial in modeling complex dynamic systems.
    • Synchronization of DRNNs is challenging due to parameter mismatches and activation function variations.
    • Finite-time control is desirable for rapid system response and stability.

    Purpose of the Study:

    • To develop an effective control strategy for finite-time synchronization of DRNNs with mismatched parameters.
    • To design a sliding mode controller that guarantees rapid convergence of synchronization errors.
    • To provide a control method that avoids computationally intensive techniques like Linear Matrix Inequalities (LMIs).

    Main Methods:

    • A novel integral sliding mode surface is designed using the drive-response concept.
    • A sliding mode controller is synthesized based on Lyapunov stability theory.
    • The controller ensures system trajectories reach the sliding surface in finite time.

    Main Results:

    • The proposed integral sliding mode surface facilitates finite-time convergence of the synchronization error to zero.
    • The designed controller guarantees that all system states are driven onto the sliding surface within a finite time.
    • The control approach is verified to be effective and does not require LMI solutions.

    Conclusions:

    • The presented sliding mode control approach effectively achieves finite-time synchronization for DRNNs with uncertainties.
    • The method offers a computationally efficient alternative for stabilizing complex neural network systems.
    • Numerical simulations confirm the practical applicability and robustness of the proposed control strategy.