A Neural-Network-Assisted Approach to Recursive State Estimation for Energy Harvesting Complex Networks With Unknown Nonlinearities

This research paper describes a method for estimating the state (current condition) of complex networks that have unknown nonlinearities (behaviors that don't follow simple linear patterns) and sensors powered by energy harvesting (collecting energy from their environment). The approach uses neural networks (machine learning models inspired by how brains process information) to approximate these unknown nonlinearities, and it only allows sensors to transmit data when they have enough stored energy to do so, helping conserve power in resource-limited systems.