Error Curve and Error Squared Curve of LMS Algorithm for Adaptive Filters

MATLAB 2K 291 views 1 downloads 1 credits
Tags:

Resource Overview

Analysis of error curve and error squared curve for LMS adaptive filtering algorithm, featuring average trajectory curve from 100 LMS iterations plotted on contour maps, with practical implementation insights complementary to Digital Signal Processing 2 curriculum

Detailed Documentation

This document explores the error curve and error squared curve of the Least Mean Squares (LMS) algorithm for adaptive filters. The implementation involves computing the error signal e(n) = d(n) - y(n) where d(n) represents the desired signal and y(n) is the filter output. We demonstrate how to plot the average trajectory curve across 100 LMS iterations on contour maps, showcasing the algorithm's convergence behavior through weight update equations w(n+1) = w(n) + μe(n)x(n). These visualizations are analyzed alongside core Digital Signal Processing 2 concepts, including filter stability conditions and step-size parameter μ selection. The discussion provides MATLAB code snippets for calculating mean-square error evolution and generating trajectory plots, enabling deeper understanding of adaptive filter implementation and DSP2 course applications. These materials are designed to support your technical learning through practical algorithm examination.

You May Also Like