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
Cet article propose des amplificateurs de lignes adaptatifs avec de nouveaux algorithmes de mise à jour des coefficients sur la base du critère des moindres carrés d'erreur. Les algorithmes adaptatifs par moindres carrés sont connus pour converger plus rapidement que ceux à gradient stochastique. Cependant, ils présentent une grande complexité de calcul en raison de l’inversion matricielle. Pour éviter l'inversion matricielle, les algorithmes proposés n'adaptent qu'un seul coefficient pour détecter une sinusoïde. Les types d'algorithmes adaptatifs FIR et IIR sont présentés, et les techniques permettant de réduire l'influence du bruit additif sont décrites dans cet article. Les amplificateurs de lignes adaptatifs proposés ont des structures simples et présentent d'excellentes caractéristiques de convergence. Alors que la convergence des algorithmes basés sur le gradient dépend largement de leurs paramètres de pas, ceux proposés en sont exempts.
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Koji MATSUURA, Eiji WATANABE, Akinori NISHIHARA, "Adaptive Line Enhancers on the Basis of Least-Squares Algorithm for a Single Sinusoid Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 8, pp. 1536-1543, August 1999, doi: .
Abstract: This paper proposes adaptive line enhancers with new coefficient update algorithms on the basis of least-square-error criteria. Adaptive algorithms by least-squares are known to converge faster than stochastic-gradient ones. However they have high computational complexity due to matrix inversion. To avoid matrix inversion the proposed algorithms adapt only one coefficient to detect one sinusoid. Both FIR and IIR types of adaptive algorithm are presented, and the techniques to reduce the influence of additive noise is described in this paper. The proposed adaptive line enhancers have simple structures and show excellent convergence characteristics. While the convergence of gradient-based algorithms largely depend on their stepsize parameters, the proposed ones are free from them.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_8_1536/_p
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@ARTICLE{e82-a_8_1536,
author={Koji MATSUURA, Eiji WATANABE, Akinori NISHIHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptive Line Enhancers on the Basis of Least-Squares Algorithm for a Single Sinusoid Detection},
year={1999},
volume={E82-A},
number={8},
pages={1536-1543},
abstract={This paper proposes adaptive line enhancers with new coefficient update algorithms on the basis of least-square-error criteria. Adaptive algorithms by least-squares are known to converge faster than stochastic-gradient ones. However they have high computational complexity due to matrix inversion. To avoid matrix inversion the proposed algorithms adapt only one coefficient to detect one sinusoid. Both FIR and IIR types of adaptive algorithm are presented, and the techniques to reduce the influence of additive noise is described in this paper. The proposed adaptive line enhancers have simple structures and show excellent convergence characteristics. While the convergence of gradient-based algorithms largely depend on their stepsize parameters, the proposed ones are free from them.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Adaptive Line Enhancers on the Basis of Least-Squares Algorithm for a Single Sinusoid Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1536
EP - 1543
AU - Koji MATSUURA
AU - Eiji WATANABE
AU - Akinori NISHIHARA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1999
AB - This paper proposes adaptive line enhancers with new coefficient update algorithms on the basis of least-square-error criteria. Adaptive algorithms by least-squares are known to converge faster than stochastic-gradient ones. However they have high computational complexity due to matrix inversion. To avoid matrix inversion the proposed algorithms adapt only one coefficient to detect one sinusoid. Both FIR and IIR types of adaptive algorithm are presented, and the techniques to reduce the influence of additive noise is described in this paper. The proposed adaptive line enhancers have simple structures and show excellent convergence characteristics. While the convergence of gradient-based algorithms largely depend on their stepsize parameters, the proposed ones are free from them.
ER -