A New Adaptive Convergence Factor Algorithm with the Constant Damping Parameter
Summary :
This paper presents a new Adaptive Convergence Factor (ACF) algorithm without the damping parameter adjustment acoording to the input signal and/or the composition of the filter system. The damping parameter in the ACF algorithms has great influence on the convergence characteristics. In order to examine the relation between the damping parameter and the convergence characteristics, the normalization which is realized by the related signal terms divided by each maximum value is introduced into the ACF algorithm. The normalized algorithm is applied to the modeling of unknown time-variable systems which makes it possible to examine the relation between the parameters and the misadjustment in the adaptive algorithms. Considering the experimental and theoretical results, the optimum value of the damping parameter can be defined as the minimum value where the total misadjustment becomes minimum. To keep the damping parameter optimum in any conditions, the new ACF algorithm is proposed by improving the invariability of the damping parameter in the normalized algorithm. The algorithm is investigated by the computer simulations in the modeling of unknown time-variable systems and the system indentification. The results of simulations show that the proposed algorithm needs no adjustment of the optimum damping parameter and brings the stable convergence characteristics even if the filter system is changed.
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- IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.6 pp.649-655
- Publication Date
- 1995/06/25
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- Special Section PAPER (Special Section of Papers Selected from 1994 Joint Technical Conference on Circuits/Systems, Computers and Communications (JTC-CSCC '94))
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Isao NAKANISHI, Yutaka FUKUI, "A New Adaptive Convergence Factor Algorithm with the Constant Damping Parameter" in IEICE TRANSACTIONS on Fundamentals,
vol. E78-A, no. 6, pp. 649-655, June 1995, doi: .
Abstract: This paper presents a new Adaptive Convergence Factor (ACF) algorithm without the damping parameter adjustment acoording to the input signal and/or the composition of the filter system. The damping parameter in the ACF algorithms has great influence on the convergence characteristics. In order to examine the relation between the damping parameter and the convergence characteristics, the normalization which is realized by the related signal terms divided by each maximum value is introduced into the ACF algorithm. The normalized algorithm is applied to the modeling of unknown time-variable systems which makes it possible to examine the relation between the parameters and the misadjustment in the adaptive algorithms. Considering the experimental and theoretical results, the optimum value of the damping parameter can be defined as the minimum value where the total misadjustment becomes minimum. To keep the damping parameter optimum in any conditions, the new ACF algorithm is proposed by improving the invariability of the damping parameter in the normalized algorithm. The algorithm is investigated by the computer simulations in the modeling of unknown time-variable systems and the system indentification. The results of simulations show that the proposed algorithm needs no adjustment of the optimum damping parameter and brings the stable convergence characteristics even if the filter system is changed.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_6_649/_p
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@ARTICLE{e78-a_6_649,
author={Isao NAKANISHI, Yutaka FUKUI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A New Adaptive Convergence Factor Algorithm with the Constant Damping Parameter},
year={1995},
volume={E78-A},
number={6},
pages={649-655},
abstract={This paper presents a new Adaptive Convergence Factor (ACF) algorithm without the damping parameter adjustment acoording to the input signal and/or the composition of the filter system. The damping parameter in the ACF algorithms has great influence on the convergence characteristics. In order to examine the relation between the damping parameter and the convergence characteristics, the normalization which is realized by the related signal terms divided by each maximum value is introduced into the ACF algorithm. The normalized algorithm is applied to the modeling of unknown time-variable systems which makes it possible to examine the relation between the parameters and the misadjustment in the adaptive algorithms. Considering the experimental and theoretical results, the optimum value of the damping parameter can be defined as the minimum value where the total misadjustment becomes minimum. To keep the damping parameter optimum in any conditions, the new ACF algorithm is proposed by improving the invariability of the damping parameter in the normalized algorithm. The algorithm is investigated by the computer simulations in the modeling of unknown time-variable systems and the system indentification. The results of simulations show that the proposed algorithm needs no adjustment of the optimum damping parameter and brings the stable convergence characteristics even if the filter system is changed.},
keywords={},
doi={},
ISSN={},
month={June},}
Copy
TY - JOUR
TI - A New Adaptive Convergence Factor Algorithm with the Constant Damping Parameter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 649
EP - 655
AU - Isao NAKANISHI
AU - Yutaka FUKUI
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 1995
AB - This paper presents a new Adaptive Convergence Factor (ACF) algorithm without the damping parameter adjustment acoording to the input signal and/or the composition of the filter system. The damping parameter in the ACF algorithms has great influence on the convergence characteristics. In order to examine the relation between the damping parameter and the convergence characteristics, the normalization which is realized by the related signal terms divided by each maximum value is introduced into the ACF algorithm. The normalized algorithm is applied to the modeling of unknown time-variable systems which makes it possible to examine the relation between the parameters and the misadjustment in the adaptive algorithms. Considering the experimental and theoretical results, the optimum value of the damping parameter can be defined as the minimum value where the total misadjustment becomes minimum. To keep the damping parameter optimum in any conditions, the new ACF algorithm is proposed by improving the invariability of the damping parameter in the normalized algorithm. The algorithm is investigated by the computer simulations in the modeling of unknown time-variable systems and the system indentification. The results of simulations show that the proposed algorithm needs no adjustment of the optimum damping parameter and brings the stable convergence characteristics even if the filter system is changed.
ER -
English
