Anti-Disturbance Proximal Neural Networks for Composite Resource Allocation

This article proposes two types of neural networks (machine learning models inspired by how brains work) designed to solve resource allocation problems (deciding how to distribute limited resources) in networked systems like smart grids. The neural networks are built to resist disturbances (unwanted interference or noise) by using different strategies: one exploits known patterns in the system, while the other uses an observer (a component that monitors the system state) to handle unexpected interference. Both networks were mathematically proven to work correctly and tested with simulations.