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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 비선형 진폭 특성을 포함하는 미지의 시스템을 식별하기 위한 적응형 알고리즘을 제안합니다. 일반적으로 비선형성은 무시할 수 있을 정도로 작습니다. 그러나 소형 스피커를 사용하는 음향 반향 제거기와 같은 저가형 시스템에서는 비선형성으로 인해 식별 성능이 저하됩니다. 따라서 열화를 방지하는 여러 방법, 다항식 또는 Volterra 계열 근사가 제안되고 연구되었습니다. 그러나 기존의 방법은 높은 처리비용을 요구한다. 본 논문에서는 조각별 선형 곡선을 사용하여 비선형 특성을 근사하는 방법을 제안하고 컴퓨터 시뮬레이션을 사용하여 성능이 크게 향상될 수 있음을 보여줍니다. 제안된 방법은 선형 적응 필터 시스템에 비해 처리 비용을 약 XNUMX배 수준으로 줄일 수 있다.
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부
Kensaku FUJII, Ryo AOKI, Mitsuji MUNEYASU, "A Low Processing Cost Adaptive Algorithm Identifying Nonlinear Unknown System with Piecewise Linear Curve" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 4, pp. 1129-1135, April 2009, doi: 10.1587/transfun.E92.A.1129.
Abstract: This paper proposes an adaptive algorithm for identifying unknown systems containing nonlinear amplitude characteristics. Usually, the nonlinearity is so small as to be negligible. However, in low cost systems, such as acoustic echo canceller using a small loudspeaker, the nonlinearity deteriorates the performance of the identification. Several methods preventing the deterioration, polynomial or Volterra series approximations, have been hence proposed and studied. However, the conventional methods require high processing cost. In this paper, we propose a method approximating the nonlinear characteristics with a piecewise linear curve and show using computer simulations that the performance can be extremely improved. The proposed method can also reduce the processing cost to only about twice that of the linear adaptive filter system.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1129/_p
부
@ARTICLE{e92-a_4_1129,
author={Kensaku FUJII, Ryo AOKI, Mitsuji MUNEYASU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Low Processing Cost Adaptive Algorithm Identifying Nonlinear Unknown System with Piecewise Linear Curve},
year={2009},
volume={E92-A},
number={4},
pages={1129-1135},
abstract={This paper proposes an adaptive algorithm for identifying unknown systems containing nonlinear amplitude characteristics. Usually, the nonlinearity is so small as to be negligible. However, in low cost systems, such as acoustic echo canceller using a small loudspeaker, the nonlinearity deteriorates the performance of the identification. Several methods preventing the deterioration, polynomial or Volterra series approximations, have been hence proposed and studied. However, the conventional methods require high processing cost. In this paper, we propose a method approximating the nonlinear characteristics with a piecewise linear curve and show using computer simulations that the performance can be extremely improved. The proposed method can also reduce the processing cost to only about twice that of the linear adaptive filter system.},
keywords={},
doi={10.1587/transfun.E92.A.1129},
ISSN={1745-1337},
month={April},}
부
TY - JOUR
TI - A Low Processing Cost Adaptive Algorithm Identifying Nonlinear Unknown System with Piecewise Linear Curve
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1129
EP - 1135
AU - Kensaku FUJII
AU - Ryo AOKI
AU - Mitsuji MUNEYASU
PY - 2009
DO - 10.1587/transfun.E92.A.1129
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2009
AB - This paper proposes an adaptive algorithm for identifying unknown systems containing nonlinear amplitude characteristics. Usually, the nonlinearity is so small as to be negligible. However, in low cost systems, such as acoustic echo canceller using a small loudspeaker, the nonlinearity deteriorates the performance of the identification. Several methods preventing the deterioration, polynomial or Volterra series approximations, have been hence proposed and studied. However, the conventional methods require high processing cost. In this paper, we propose a method approximating the nonlinear characteristics with a piecewise linear curve and show using computer simulations that the performance can be extremely improved. The proposed method can also reduce the processing cost to only about twice that of the linear adaptive filter system.
ER -