Machine Learning Approaches for Dynamic System Parameter Estimation in Sensor Networks

Abstract

Machine learning approaches for dynamic system parameter estimation in sensor networks involve the utilization of algorithms and models to infer and track the evolving parameters of dynamic systems using sensor data. These methods often employ techniques such as Bayesian inference and Sensor Networks to adaptively learn the underlying system dynamics and estimate parameters in real time. By leveraging the rich information gathered from sensor networks, these approaches can address challenges such as non-linearity, noise, and changing environmental conditions. They enable robust and accurate estimation of system parameters, facilitating various applications ranging from environmental monitoring to industrial process control. Additionally, the inherent flexibility of machine learning allows for the development of adaptive algorithms capable of accommodating the evolving nature of dynamic systems, ensuring continuous and precise parameter estimation in sensor networks.