The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Un filtre numérique adaptatif LMS utilisant l'arithmétique distribuée (DA-ADF) a été proposé. Cowan et d'autres ont proposé l'algorithme adaptatif DA avec codage binaire décalé pour la dérivation simple d'un algorithme et l'utilisation d'une propriété de symétrie impaire de l'espace fonctionnel adaptatif (AFS). Cependant, nous avons indiqué que la vitesse de convergence de cet algorithme adaptatif DA était extrêmement dégradée par nos simulations informatiques. Pour surmonter ces problèmes, nous avons proposé l'algorithme adaptatif DA généralisé avec une représentation en complément à deux et des architectures efficaces. Notre DA-ADF présente simultanément des performances de vitesse élevée, une faible latence de sortie, une bonne vitesse de convergence, un matériel à petite échelle et une dissipation de puissance inférieure pour un ordre supérieur. Dans cet article, nous analysons une condition de convergence de l’algorithme adaptatif DA qui n’a jamais été considérée théoriquement. De cette analyse, nous indiquons que la vitesse de convergence dépend d'une distribution de valeurs propres d'une matrice d'autocorrélation d'un vecteur de signal d'entrée étendu. De plus, nous obtenons théoriquement les valeurs propres. En conséquence, nous montrons clairement que notre DA-ADF présente un avantage par rapport au DA-ADF conventionnel en termes de vitesse de convergence.
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Kyo TAKAHASHI, Yoshitaka TSUNEKAWA, Norio TAYAMA, Kyoushirou SEKI, "Analysis of the Convergence Condition of LMS Adaptive Digital Filter Using Distributed Arithmetic" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 6, pp. 1249-1256, June 2002, doi: .
Abstract: An LMS adaptive digital filter using distributed arithmetic (DA-ADF) has been proposed. Cowan and others proposed the DA adaptive algorithm with offset binary coding for the simple derivation of an algorithm and the use of an odd-symmetry property of adaptive function space (AFS). However, we indicated that a convergence speed of this DA adaptive algorithm degraded extremely by our computer simulations. To overcome these problems, we have proposed the DA adaptive algorithm generalized with two's complement representation and effective architectures. Our DA-ADF has performances of a high speed, small output latency, a good convergence speed, small-scale hardware and lower power dissipation for higher order, simultaneously. In this paper, we analyze a convergence condition of DA adaptive algorithm that has never been considered theoretically. From this analysis, we indicate that the convergence speed is depended on a distribution of eigenvalues of an auto-correlation matrix of an extended input signal vector . Furthermore, we obtain the eigenvalues theoretically. As a result, we clearly show that our DA-ADF has an advantage of the conventional DA-ADF in the convergence speed.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_6_1249/_p
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@ARTICLE{e85-a_6_1249,
author={Kyo TAKAHASHI, Yoshitaka TSUNEKAWA, Norio TAYAMA, Kyoushirou SEKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Analysis of the Convergence Condition of LMS Adaptive Digital Filter Using Distributed Arithmetic},
year={2002},
volume={E85-A},
number={6},
pages={1249-1256},
abstract={An LMS adaptive digital filter using distributed arithmetic (DA-ADF) has been proposed. Cowan and others proposed the DA adaptive algorithm with offset binary coding for the simple derivation of an algorithm and the use of an odd-symmetry property of adaptive function space (AFS). However, we indicated that a convergence speed of this DA adaptive algorithm degraded extremely by our computer simulations. To overcome these problems, we have proposed the DA adaptive algorithm generalized with two's complement representation and effective architectures. Our DA-ADF has performances of a high speed, small output latency, a good convergence speed, small-scale hardware and lower power dissipation for higher order, simultaneously. In this paper, we analyze a convergence condition of DA adaptive algorithm that has never been considered theoretically. From this analysis, we indicate that the convergence speed is depended on a distribution of eigenvalues of an auto-correlation matrix of an extended input signal vector . Furthermore, we obtain the eigenvalues theoretically. As a result, we clearly show that our DA-ADF has an advantage of the conventional DA-ADF in the convergence speed.},
keywords={},
doi={},
ISSN={},
month={June},}
Copier
TY - JOUR
TI - Analysis of the Convergence Condition of LMS Adaptive Digital Filter Using Distributed Arithmetic
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1249
EP - 1256
AU - Kyo TAKAHASHI
AU - Yoshitaka TSUNEKAWA
AU - Norio TAYAMA
AU - Kyoushirou SEKI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2002
AB - An LMS adaptive digital filter using distributed arithmetic (DA-ADF) has been proposed. Cowan and others proposed the DA adaptive algorithm with offset binary coding for the simple derivation of an algorithm and the use of an odd-symmetry property of adaptive function space (AFS). However, we indicated that a convergence speed of this DA adaptive algorithm degraded extremely by our computer simulations. To overcome these problems, we have proposed the DA adaptive algorithm generalized with two's complement representation and effective architectures. Our DA-ADF has performances of a high speed, small output latency, a good convergence speed, small-scale hardware and lower power dissipation for higher order, simultaneously. In this paper, we analyze a convergence condition of DA adaptive algorithm that has never been considered theoretically. From this analysis, we indicate that the convergence speed is depended on a distribution of eigenvalues of an auto-correlation matrix of an extended input signal vector . Furthermore, we obtain the eigenvalues theoretically. As a result, we clearly show that our DA-ADF has an advantage of the conventional DA-ADF in the convergence speed.
ER -