ADAPTIVE SIGNAL FILTERING USING MACHINE LEARNING TECHNIQUES

Abstract

Adaptive signal filtering is a fundamental operation in communication systems, biomedical signal processing, audio enhancement, and control applications, where signals are often corrupted by noise, interference, and non-stationary disturbances. Conventional adaptive filtering algorithms such as Least Mean Squares (LMS) and Recursive Least Squares (RLS) rely on predefined mathematical models and struggle to maintain optimal performance under highly dynamic and nonlinear environments. This paper presents a machine learning-based adaptive signal filtering framework that leverages data-driven learning to enhance noise suppression and signal estimation accuracy. By integrating supervised learning models with adaptive filtering principles, the proposed approach improves convergence speed and robustness. MATLAB/Simulink-based simulations demonstrate superior filtering performance compared to traditional adaptive filters, validating the effectiveness of machine learning techniques for adaptive signal filtering applications.