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
Um filtro digital adaptativo LMS usando aritmética distribuída (DA-ADF) foi proposto. Cowan e outros propuseram o algoritmo adaptativo DA com codificação binária offset para a derivação simples de um algoritmo e o uso de uma propriedade de simetria ímpar do espaço de função adaptativo (AFS). No entanto, indicamos que a velocidade de convergência deste algoritmo adaptativo DA foi extremamente degradada pelas nossas simulações computacionais. Para superar esses problemas, propusemos o algoritmo adaptativo DA generalizado com representação em complemento de dois e arquiteturas efetivas. Nosso DA-ADF possui desempenho de alta velocidade, pequena latência de saída, boa velocidade de convergência, hardware de pequena escala e menor dissipação de energia para pedidos superiores, simultaneamente. Neste artigo, analisamos uma condição de convergência do algoritmo adaptativo DA que nunca foi considerada teoricamente. A partir desta análise, indicamos que a velocidade de convergência depende de uma distribuição de autovalores de uma matriz de autocorrelação de um vetor de sinal de entrada estendido. Além disso, obtemos os autovalores teoricamente. Como resultado, mostramos claramente que nosso DA-ADF tem uma vantagem sobre o DA-ADF convencional na velocidade de convergência.
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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},}
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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 -