LMS Adaptive Filter Algorithm Implementation

This document presents a MATLAB program example that utilizes the LMS algorithm to identify linear systems corrupted by noise. The implementation demonstrates an adaptive filtering approach designed to mitigate noise interference and enhance system performance. The core algorithm operates by continuously updating filter coefficients based on the error difference between the input signal and the desired output signal. Through iterative adjustments using the steepest descent method, the filter coefficients gradually converge toward optimal values that achieve the target filtering effect. Key implementation aspects include: - Step size parameter (μ) selection for stability and convergence rate - Real-time coefficient update equation: w(n+1) = w(n) + μ·e(n)·x(n) - Error signal calculation and performance monitoring This experiment provides practical insights into adaptive filter principles and their applications in signal processing. The code includes demonstration scenarios showing how the LMS filter adapts to changing system conditions and noise characteristics. May this example inspire further exploration in your learning and research endeavors!