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New variable step-sizes minimizing mean-square deviation for the lms-type algorithms

journal contribution
posted on 2014-02-13, 00:00 authored by S Zhao, D L Jones, Sui Yang KhooSui Yang Khoo, Z Man
The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms. © 2014 Springer Science+Business Media New York.

History

Journal

Circuits, systems, and signal processing

Volume

33

Issue

7

Pagination

2251 - 2265

Publisher

Springer

Location

Berlin, Germany

ISSN

0278-081X

eISSN

1531-5878

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2014, Springer