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
Este artigo propõe intensificadores de linha adaptativos com novos algoritmos de atualização de coeficientes com base em critérios de erro mínimo quadrado. Sabe-se que algoritmos adaptativos por mínimos quadrados convergem mais rapidamente do que algoritmos de gradiente estocástico. Porém eles possuem alta complexidade computacional devido à inversão de matrizes. Para evitar a inversão de matrizes os algoritmos propostos adaptam apenas um coeficiente para detectar uma senóide. Os tipos de algoritmo adaptativo FIR e IIR são apresentados, e as técnicas para reduzir a influência do ruído aditivo são descritas neste artigo. Os intensificadores de linha adaptativos propostos possuem estruturas simples e apresentam excelentes características de convergência. Embora a convergência de algoritmos baseados em gradiente dependa em grande parte de seus parâmetros de tamanho de passo, os propostos estão livres deles.
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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 -