A Convex Combination LMS Algorithm Based on Krylov Subspace Transform

Author

Li, Ning ; Zhang, Yonggang ; Wang, Chengcheng

Author_Institution

Coll. of Autom., Harbin Eng. Univ., Harbin, China

fYear

2012

fDate

23-26 June 2012

Firstpage

802

Lastpage

805

Abstract

A convex combination LMS (least mean square) algorithm based on Krylov subspace transform is proposed in this paper. In this approach, impulse response of the unknown system is firstly transformed into Krylov subspace, in which the system structure is changed into sparse. Then an improved proportionate normalized LMS (IPNLMS) algorithm and a variable tap-length normalized LMS (VTNLMS) algorithm are combined. Simulations are performed to show the convergence performance of the combined algorithm. The results show that both a fast convergence rate and small steady state mean square deviation (MSD) are obtained.

Keywords

adaptive filters; convergence of numerical methods; least mean squares methods; linear algebra; transforms; transient response; IPNLMS algorithm; Krylov subspace transform; MSD; VTNLMS algorithm; adaptive filters; convergence performance; convex combination LMS algorithm; fast convergence rate; improved proportionate normalized LMS algorithm; impulse response; least mean squares algorithm; steady state mean square deviation; variable tap-length normalized LMS algorithm; Algorithm design and analysis; Convergence; Least squares approximation; Signal processing algorithms; Steady-state; Transforms; Vectors; Adaptive filters; Krylov subspace; convex combination;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4673-1365-0

Type

conf

DOI

10.1109/CSO.2012.180

Filename

6274844