Convergence Analysis of Quantizing Method with Correlated Gaussian Data

Kiyoshi TAKAHASHI; Noriyoshi KUROYANAGI; Shinsaku MORI

Convergence Analysis of Quantizing Method with Correlated Gaussian Data

Kiyoshi TAKAHASHI, Noriyoshi KUROYANAGI, Shinsaku MORI

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In this paper the normalized lease mean square (NLMS) algorithm based on clipping input samples with an arbitrary threshold level is studied. The convergence characteristics of these clipping algorithms with correlated data are presented. In the clipping algorithm, the input samples are clipped only when the input samples are greater than or equal to the threshold level and otherwise the input samples are set to zero. The results of the analysis yield that the gain constant to ensure convergence, the speed of the convergence, and the misadjustment are functions of the threshold level. Furthermore an optimum threshold level is derived in terms of the convergence speed under the condition of the constant misadjustment.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.8 pp.1157-1165

Publication Date: 1996/08/25

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Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)

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Kiyoshi TAKAHASHI, Noriyoshi KUROYANAGI, Shinsaku MORI, "Convergence Analysis of Quantizing Method with Correlated Gaussian Data" in IEICE TRANSACTIONS on Fundamentals, vol. E79-A, no. 8, pp. 1157-1165, August 1996, doi: .
Abstract: In this paper the normalized lease mean square (NLMS) algorithm based on clipping input samples with an arbitrary threshold level is studied. The convergence characteristics of these clipping algorithms with correlated data are presented. In the clipping algorithm, the input samples are clipped only when the input samples are greater than or equal to the threshold level and otherwise the input samples are set to zero. The results of the analysis yield that the gain constant to ensure convergence, the speed of the convergence, and the misadjustment are functions of the threshold level. Furthermore an optimum threshold level is derived in terms of the convergence speed under the condition of the constant misadjustment.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_8_1157/_p

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@ARTICLE{e79-a_8_1157,
author={Kiyoshi TAKAHASHI, Noriyoshi KUROYANAGI, Shinsaku MORI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Convergence Analysis of Quantizing Method with Correlated Gaussian Data},
year={1996},
volume={E79-A},
number={8},
pages={1157-1165},
abstract={In this paper the normalized lease mean square (NLMS) algorithm based on clipping input samples with an arbitrary threshold level is studied. The convergence characteristics of these clipping algorithms with correlated data are presented. In the clipping algorithm, the input samples are clipped only when the input samples are greater than or equal to the threshold level and otherwise the input samples are set to zero. The results of the analysis yield that the gain constant to ensure convergence, the speed of the convergence, and the misadjustment are functions of the threshold level. Furthermore an optimum threshold level is derived in terms of the convergence speed under the condition of the constant misadjustment.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - Convergence Analysis of Quantizing Method with Correlated Gaussian Data
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1157
EP - 1165
AU - Kiyoshi TAKAHASHI
AU - Noriyoshi KUROYANAGI
AU - Shinsaku MORI
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1996
AB - In this paper the normalized lease mean square (NLMS) algorithm based on clipping input samples with an arbitrary threshold level is studied. The convergence characteristics of these clipping algorithms with correlated data are presented. In the clipping algorithm, the input samples are clipped only when the input samples are greater than or equal to the threshold level and otherwise the input samples are set to zero. The results of the analysis yield that the gain constant to ensure convergence, the speed of the convergence, and the misadjustment are functions of the threshold level. Furthermore an optimum threshold level is derived in terms of the convergence speed under the condition of the constant misadjustment.
ER -