Anti-Disturbance Proximal Neural Networks for Composite Resource Allocation

Composite resource allocation problems with general constraints are studied in this article, which frequently arise in networked systems such as smart grids and multiagent coordination. To address the challenges posed by multivalued differential inclusions resulting from nonsmooth objective functions, two anti-disturbance proximal neural networks are proposed, each tailored to handle structured and unstructured disturbances, respectively. For structured disturbances, the neural network is develo