Enhanced LMS Algorithm with Application in Noise Cancellation

An improved LMS algorithm and its application in noise cancellation. Building upon traditional fixed-step and variable-step LMS algorithms, this enhanced variable-step method utilizes exponential cubic instantaneous error magnitude with a forgetting factor for simultaneous step-size adjustment, effectively balancing convergence speed and steady-state error trade-offs.

MATLAB Implementation of LMS Algorithm and Enhanced LMS Algorithm with Source Code

MATLAB source code implementation of the Least Mean Squares (LMS) algorithm and its improved variants for adaptive filtering applications in signal processing and communications.

LMS Algorithm and Its Improved Variants in Adaptive Filtering

This analysis compares the core LMS algorithm with its improved variants including Normalized LMS (NLMS), Variable Step-Size LMS, and Transform-Domain LMS algorithms, examining their key differences and computational characteristics. It further extends the traditional LMS algorithm's applications and provides a comparative analysis with RLS algorithm properties, highlighting performance trade-offs in convergence speed and computational complexity.

Improved LMS Algorithm - Normalized LMS Algorithm

This program demonstrates an enhanced version of the least mean squares (LMS) algorithm - the normalized LMS algorithm, which offers superior convergence properties compared to traditional LMS. The implementation includes comparative plots showing performance differences, with code-based analysis of key parameters like step size normalization and error calculation.