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Anti-Disturbance Proximal Neural Networks for Composite Resource Allocation
IEEE Transactions on Neural Networks and Learning Systems
|March 25, 2026
Summary
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.
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.
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