Anti-Disturbance Proximal Neural Networks for Composite Resource Allocation

Summary

This article addresses composite resource allocation problems with general constraints, common in networked systems such as smart grids and multiagent coordination. It proposes two anti-disturbance proximal neural networks: one for structured disturbances, built on the internal model principle, and an observer-based one for unstructured disturbances. Asymptotic convergence of both is established via Lyapunov stability theory, and numerical simulations validate their effectiveness and robustness.