IEICE TRANSACTIONS on Fundamentals
The LMS-Type Adaptive Filter Based on the Gaussian Model for Controlling the Variances of Coefficients
Summary :
In this paper, we propose a method which enables us to control the variance of the coefficients of the LMS-type adaptive filters. In the method, each coefficient of the adaptive filter is modeled as an random variable with a Gaussian distribution, and its value is estimated as the mean value of the distribution. Besides, at each time, we check if the updated value exists within the predefined range of distribution. The update of a coefficient will be canceled when its updated value exceeds the range. We propose an implementation method which has similar formula as the Gaussian mixture model (GMM) widely used in signal processing and machine learning. The effectiveness of the proposed method is evaluated by the computer simulations.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.12 pp.1494-1502
- Publication Date
- 2020/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020SMP0011
- Type of Manuscript
- Special Section PAPER (Special Section on Smart Multimedia & Communication Systems)
- Category
- Digital Signal Processing
Authors
Kiyoshi NISHIKAWA
Tokyo Metropolitan University
Keyword
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Kiyoshi NISHIKAWA, "The LMS-Type Adaptive Filter Based on the Gaussian Model for Controlling the Variances of Coefficients" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 12, pp. 1494-1502, December 2020, doi: 10.1587/transfun.2020SMP0011.
Abstract: In this paper, we propose a method which enables us to control the variance of the coefficients of the LMS-type adaptive filters. In the method, each coefficient of the adaptive filter is modeled as an random variable with a Gaussian distribution, and its value is estimated as the mean value of the distribution. Besides, at each time, we check if the updated value exists within the predefined range of distribution. The update of a coefficient will be canceled when its updated value exceeds the range. We propose an implementation method which has similar formula as the Gaussian mixture model (GMM) widely used in signal processing and machine learning. The effectiveness of the proposed method is evaluated by the computer simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020SMP0011/_p
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@ARTICLE{e103-a_12_1494,
author={Kiyoshi NISHIKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={The LMS-Type Adaptive Filter Based on the Gaussian Model for Controlling the Variances of Coefficients},
year={2020},
volume={E103-A},
number={12},
pages={1494-1502},
abstract={In this paper, we propose a method which enables us to control the variance of the coefficients of the LMS-type adaptive filters. In the method, each coefficient of the adaptive filter is modeled as an random variable with a Gaussian distribution, and its value is estimated as the mean value of the distribution. Besides, at each time, we check if the updated value exists within the predefined range of distribution. The update of a coefficient will be canceled when its updated value exceeds the range. We propose an implementation method which has similar formula as the Gaussian mixture model (GMM) widely used in signal processing and machine learning. The effectiveness of the proposed method is evaluated by the computer simulations.},
keywords={},
doi={10.1587/transfun.2020SMP0011},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - The LMS-Type Adaptive Filter Based on the Gaussian Model for Controlling the Variances of Coefficients
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1494
EP - 1502
AU - Kiyoshi NISHIKAWA
PY - 2020
DO - 10.1587/transfun.2020SMP0011
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2020
AB - In this paper, we propose a method which enables us to control the variance of the coefficients of the LMS-type adaptive filters. In the method, each coefficient of the adaptive filter is modeled as an random variable with a Gaussian distribution, and its value is estimated as the mean value of the distribution. Besides, at each time, we check if the updated value exists within the predefined range of distribution. The update of a coefficient will be canceled when its updated value exceeds the range. We propose an implementation method which has similar formula as the Gaussian mixture model (GMM) widely used in signal processing and machine learning. The effectiveness of the proposed method is evaluated by the computer simulations.
ER -
English
