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Related Experiment Video

Updated: Mar 27, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Anti-Disturbance Proximal Neural Networks for Composite Resource Allocation.

Linhua Luan, Shuai Qi, Sitian Qin

    IEEE Transactions on Neural Networks and Learning Systems
    |March 25, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces two novel anti-disturbance proximal neural networks to solve complex resource allocation problems in networked systems. The proposed methods effectively handle both structured and unstructured disturbances, ensuring system stability and robustness.

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    Last Updated: Mar 27, 2026

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Composite resource allocation problems are common in networked systems like smart grids and multiagent coordination.
    • Nonsmooth objective functions lead to challenges like multivalued differential inclusions.

    Purpose of the Study:

    • To propose novel anti-disturbance proximal neural networks for composite resource allocation problems.
    • To address challenges posed by structured and unstructured disturbances in these systems.

    Main Methods:

    • Development of an internal model principle-based neural network for structured disturbances.
    • Design of an observer-based neural network for unstructured disturbances.
    • Rigorous convergence analysis using Lyapunov stability theory.

    Main Results:

    • Both proposed neural networks demonstrate asymptotic convergence.
    • Numerical simulations validate the effectiveness and robustness against various disturbances.

    Conclusions:

    • The proposed anti-disturbance proximal neural networks offer a robust solution for composite resource allocation problems.
    • These networks enhance system resilience in networked environments facing complex disturbances.