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Distributed Adaptive Tracking Synchronization for Coupled Reaction-Diffusion Neural Network.

Hao Zhang, Nikhil R Pal, Yin Sheng

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    Summary
    This summary is machine-generated.

    This study addresses tracking synchronization for coupled reaction-diffusion neural networks (CRDNNs). Adaptive control strategies are proposed to achieve synchronization, enhancing network robustness against disturbances.

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    Area of Science:

    • Control Theory
    • Neural Networks
    • Dynamical Systems

    Background:

    • Coupled reaction-diffusion neural networks (CRDNNs) are complex systems with applications in various fields.
    • Achieving tracking synchronization in CRDNNs is crucial for their effective utilization.
    • Existing methods may face challenges with varying network dynamics and control costs.

    Purpose of the Study:

    • To develop novel adaptive control strategies for tracking synchronization in CRDNNs.
    • To address synchronization challenges when network nodes have dynamics identical or different from the tracking trajectory.
    • To enhance the robustness of CRDNNs against external disturbances.

    Main Methods:

    • Proposed edge-based and vertex-based adaptive strategies for coupling strengths and controllers.
    • Employed adaptive pinning control to reduce control costs.
    • Developed a vertex-based adaptive strategy for cases with differing individual dynamics.

    Main Results:

    • Successfully achieved tracking synchronization for CRDNNs with identical individual dynamics using local information.
    • Demonstrated the ability to drive synchronization error to a small, adjustable area for differing dynamics.
    • Validated the effectiveness of the adaptive design in enhancing network robustness.

    Conclusions:

    • The proposed adaptive strategies effectively achieve tracking synchronization in CRDNNs under various dynamic conditions.
    • Adaptive pinning control offers a cost-effective approach to synchronization.
    • The adaptive design improves network resilience to external disturbances.