Modified Normalized Least Mean Square Algorithm with Improved Minimization Criterion

Author

Sawale, M.D. ; Yadav, R.N.

Author_Institution

Dept. of Electron. & Commun., Maulana Azad Nat. Inst. of Technol., Bhopal, India

fYear

2011

fDate

7-9 Oct. 2011

Firstpage

541

Lastpage

544

Abstract

In this paper we develop an improved minimization criterion for normalized least mean squares (NLMS) algorithm using past weight vectors and adaptive learning rate. The proposed criterion minimizes the summation of each squared Euclidean norm of difference between the currently updated weight vector and past weight vector. The result of the modified NLMS algorithm has lower misalignment than the conventional NLMS algorithm for various SNR. The simulation shows that the convergence rate of proposed NLMS algorithm is faster as the previous weight vectors and SNR increases.

Keywords

adaptive filters; least squares approximations; minimisation; vectors; Euclidean norm; adaptive learning rate; minimization criterion; normalized least mean square algorithm; past weight vectors; Adaptation models; Algorithm design and analysis; Convergence; Minimization; Signal processing algorithms; Signal to noise ratio; Vectors; Learning rate; Mean square deviation; Minimization criterion; Normalized least mean squares algorithm; Weight vector;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Communication Networks (CICN), 2011 International Conference on

Conference_Location

Gwalior

Print_ISBN

978-1-4577-2033-8

Type

conf

DOI

10.1109/CICN.2011.116

Filename

6112927