Exploring Kalman Filtering Applications for Enhancing Artificial Neural Network Learning

Kalman filter is a widely used estimation algorithm with numerous applications, including parameter estimation, classification, prediction, pattern recognition, tuning, and filtering. Recently, it has gained attention in artificial intelligence and machine learning as a mathematical framework for the learning process. As a methodology designed for stochastic environments, the Kalman filter effectively manages noise and unstructured data with incomplete information while preventing premature stagnation, enabling faster learning and reducing the need for extensive pre-processing. These characteristics make it ideal for training artificial neural networks and other machine learning techniques. Given its significance, this paper presents a review of Kalman filter applications for artificial neural network learning.