MATLAB Simulation of Adaptive Filter Using LMS Algorithm

This article presents a MATLAB simulation of an adaptive filter implemented using the Least Mean Squares (LMS) algorithm. The simulation allows users to adjust key parameters including filter order and convergence factor (step size), providing flexibility for various application scenarios. The implementation typically involves initializing filter coefficients, processing input signals through adaptive weighting, and updating coefficients using the LMS weight adjustment formula: w(n+1) = w(n) + μ·e(n)·x(n), where μ represents the convergence factor, e(n) is the error signal, and x(n) is the input vector. Through this simulation tool, users can gain deeper insights into adaptive filter principles, observe convergence behavior, and analyze performance metrics like mean square error. This resource aims to provide practical guidance for effectively implementing adaptive filters in real engineering applications, with particular focus on parameter tuning and algorithm behavior visualization.