Source Code for Least Mean Square (LMS) Filter Implementation

This package provides the complete source code for implementing Least Mean Square (LMS) adaptive filtering algorithms in MATLAB. The code is designed for immediate execution without requiring additional modifications. The LMS filter represents a fundamental adaptive signal processing technique that efficiently reduces noise while preserving meaningful signal components through gradient-based weight adaptation. The implementation includes core MATLAB functions demonstrating: - Real-time filter coefficient updates using the Widrow-Hoff delta rule - Step-size parameter (mu) optimization for convergence control - Vectorized operations for computational efficiency - Multiple input signal handling capabilities A comprehensive technical documentation accompanies the source files, detailing: 1. Algorithm initialization procedures and parameter settings 2. Mathematical derivation of the weight adaptation formula: w(n+1) = w(n) + μ·e(n)·x(n) 3. Practical implementation considerations for stability and convergence 4. Example applications showcasing noise cancellation and system identification The resource serves both educational and practical purposes, offering: - Beginners: Step-by-step guidance through adaptive filter concepts - Experienced engineers: Optimized code structures for research and development projects - All users: Experiment-ready templates for custom modifications and performance analysis The package architecture ensures straightforward integration into existing MATLAB workflows while maintaining modularity for algorithm extensions and comparative studies.