MATLAB Implementation of Adaptive Filter Using LMS Steepest Descent Method

This document provides original MATLAB source code for implementing adaptive filters using the LMS (Least Mean Squares) steepest descent method. The implementation includes core functions for adaptive filtering applications, featuring real-time coefficient updates and error minimization. The code demonstrates key algorithmic components including: - Weight vector initialization and update procedures - Step size parameter (μ) configuration for convergence control - Error signal computation and filtering operations - Iterative coefficient adjustment using the steepest descent approach The implementation follows the standard LMS algorithm structure with input signal processing, desired signal comparison, and adaptive weight adaptation. Users can modify parameters such as filter length, step size, and convergence criteria based on specific application requirements. Whether for academic research or practical applications, this code provides a foundational framework for understanding adaptive filtering principles and LMS algorithm behavior. The implementation includes proper vectorization techniques for efficient MATLAB execution and contains comments explaining critical algorithm steps. You can download and utilize this code to integrate adaptive filtering capabilities into your signal processing projects, analyze algorithm performance, and customize the implementation for specific use cases. The code structure allows for easy integration with existing MATLAB signal processing workflows and includes examples of typical input/output configurations. Through this implementation, users can gain deeper insights into adaptive filter operation principles and apply the LMS steepest descent method effectively in various engineering applications.