A PERFORMANCE ANALYSIS OF LMS, RLS AND LATTICE BASEDALGORITHMS AS APPLIED TO THE AREA OF LINEAR PREDICTION

Journal of Global Research in Computer Sciences

article

Nasrin Akhter¹  Lilatul Ferdouse¹  Fariha Tasmin Jaigirdar¹  Tamanna Haque Nipa¹ 

[1] Department OF Computer Science, Stamford University Bangladesh

关键词: Adaptive filtering;    Linear Prediction;    LMS;    RLS;    Lattice based algorithms;    SNR.;   

来源: Research & Reviews

【 摘 要 】

This paper presents a performance analysis of three categories of adaptive filtering algorithms in the application of linear prediction. The classes of algorithms considered are Least-Mean-Square (LMS) based, Recursive Least-Squares (RLS) based and Lattice based adaptive filtering algorithms. The performances of the algorithms in each class are compared in terms of convergence behavior, execution time and filter length. The analysis determines the best converging algorithm from each class. Finally the best performing algorithm for adaptive linear prediction is selected.

【 授权许可】

Unknown