Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Sensor fusion has become an integral part of modern industrial control systems, where data from multiple sensors are combined to create a more accurate and reliable system response. This paper discusses the analytical methods used for sensor fusion, including Kalman filters, Bayesian networks, and machine learning techniques. The paper highlights how sensor fusion improves decision-making processes in control engineering by reducing noise and uncertainty in sensor data. Practical applications in fields like robotics, autonomous systems, and manufacturing processes are provided to illustrate the advantages of different fusion techniques.

Keywords: Sensor Fusion, Kalman Filters, Bayesian Networks, Machine Learning, Industrial Control