IEICE TRANSACTIONS on Communications
A Modified Variable Error-Data Normalized Step-Size LMS Adaptive Filter Algorithm
Summary :
This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E92-B No.12 pp.3903-3906
- Publication Date
- 2009/12/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E92.B.3903
- Type of Manuscript
- LETTER
- Category
- Fundamental Theories for Communications
Authors
Keyword
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Chee-Hyun PARK, Kwang-Seok HONG, "A Modified Variable Error-Data Normalized Step-Size LMS Adaptive Filter Algorithm" in IEICE TRANSACTIONS on Communications,
vol. E92-B, no. 12, pp. 3903-3906, December 2009, doi: 10.1587/transcom.E92.B.3903.
Abstract: This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E92.B.3903/_p
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@ARTICLE{e92-b_12_3903,
author={Chee-Hyun PARK, Kwang-Seok HONG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Modified Variable Error-Data Normalized Step-Size LMS Adaptive Filter Algorithm},
year={2009},
volume={E92-B},
number={12},
pages={3903-3906},
abstract={This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.},
keywords={},
doi={10.1587/transcom.E92.B.3903},
ISSN={1745-1345},
month={December},}
Copy
TY - JOUR
TI - A Modified Variable Error-Data Normalized Step-Size LMS Adaptive Filter Algorithm
T2 - IEICE TRANSACTIONS on Communications
SP - 3903
EP - 3906
AU - Chee-Hyun PARK
AU - Kwang-Seok HONG
PY - 2009
DO - 10.1587/transcom.E92.B.3903
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E92-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2009
AB - This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.
ER -
English
