A Fast Adaptive Algorithm Using Gradient Vectors of Multiple ADF

Kei IKEDA; Mitsutoshi HATORI; Kiyoharu AIZAWA

A Fast Adaptive Algorithm Using Gradient Vectors of Multiple ADF

Kei IKEDA, Mitsutoshi HATORI, Kiyoharu AIZAWA

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The inherent simplicity of the LMS (Least Mean Square) Algorithm has lead to its wide usage. However, it is well known that high speed convergence and low final misadjustment cannot be realized simultaneously by the conventional LMS method. To overcome this trade-off problem, a new adaptive algorithm using Multiple ADF's (Adaptive Digital Filters) is proposed. The proposed algorithm modifies coefficients using multiple gradient vectors of the squared error, which are computed at different points on the performance surface. First, the proposed algorithm using 2 ADF's is discussed. Simulation results show that both high speed convergence and low final misadjustment can be realized. The computation time of this proposed algorithm is nearly as much as that of LMS if parallel processing techniques are used. Moreover, the proposed algorithm using more than 2 ADF's is discussed. It is understood that if more than 2 ADF's are used, further improvement in the convergence speed in not realized, but a reduction of the final misadjustment and an improvement in the stability are realized. Finally, a method which can improve the convergence property in the presence of correlated input is discussed. It is indicated that using priori knowledge and matrix transformation, the convergence property is quite improved even when a strongly correlated signal input is applied.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E75-A No.8 pp.972-979

Publication Date: 1992/08/25

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Type of Manuscript: Special Section PAPER (Special Section on the 6th Digital Signal Processing Symposium)

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Kei IKEDA, Mitsutoshi HATORI, Kiyoharu AIZAWA, "A Fast Adaptive Algorithm Using Gradient Vectors of Multiple ADF" in IEICE TRANSACTIONS on Fundamentals, vol. E75-A, no. 8, pp. 972-979, August 1992, doi: .
Abstract: The inherent simplicity of the LMS (Least Mean Square) Algorithm has lead to its wide usage. However, it is well known that high speed convergence and low final misadjustment cannot be realized simultaneously by the conventional LMS method. To overcome this trade-off problem, a new adaptive algorithm using Multiple ADF's (Adaptive Digital Filters) is proposed. The proposed algorithm modifies coefficients using multiple gradient vectors of the squared error, which are computed at different points on the performance surface. First, the proposed algorithm using 2 ADF's is discussed. Simulation results show that both high speed convergence and low final misadjustment can be realized. The computation time of this proposed algorithm is nearly as much as that of LMS if parallel processing techniques are used. Moreover, the proposed algorithm using more than 2 ADF's is discussed. It is understood that if more than 2 ADF's are used, further improvement in the convergence speed in not realized, but a reduction of the final misadjustment and an improvement in the stability are realized. Finally, a method which can improve the convergence property in the presence of correlated input is discussed. It is indicated that using priori knowledge and matrix transformation, the convergence property is quite improved even when a strongly correlated signal input is applied.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e75-a_8_972/_p

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@ARTICLE{e75-a_8_972,
author={Kei IKEDA, Mitsutoshi HATORI, Kiyoharu AIZAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Fast Adaptive Algorithm Using Gradient Vectors of Multiple ADF},
year={1992},
volume={E75-A},
number={8},
pages={972-979},
abstract={The inherent simplicity of the LMS (Least Mean Square) Algorithm has lead to its wide usage. However, it is well known that high speed convergence and low final misadjustment cannot be realized simultaneously by the conventional LMS method. To overcome this trade-off problem, a new adaptive algorithm using Multiple ADF's (Adaptive Digital Filters) is proposed. The proposed algorithm modifies coefficients using multiple gradient vectors of the squared error, which are computed at different points on the performance surface. First, the proposed algorithm using 2 ADF's is discussed. Simulation results show that both high speed convergence and low final misadjustment can be realized. The computation time of this proposed algorithm is nearly as much as that of LMS if parallel processing techniques are used. Moreover, the proposed algorithm using more than 2 ADF's is discussed. It is understood that if more than 2 ADF's are used, further improvement in the convergence speed in not realized, but a reduction of the final misadjustment and an improvement in the stability are realized. Finally, a method which can improve the convergence property in the presence of correlated input is discussed. It is indicated that using priori knowledge and matrix transformation, the convergence property is quite improved even when a strongly correlated signal input is applied.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - A Fast Adaptive Algorithm Using Gradient Vectors of Multiple ADF
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 972
EP - 979
AU - Kei IKEDA
AU - Mitsutoshi HATORI
AU - Kiyoharu AIZAWA
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E75-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1992
AB - The inherent simplicity of the LMS (Least Mean Square) Algorithm has lead to its wide usage. However, it is well known that high speed convergence and low final misadjustment cannot be realized simultaneously by the conventional LMS method. To overcome this trade-off problem, a new adaptive algorithm using Multiple ADF's (Adaptive Digital Filters) is proposed. The proposed algorithm modifies coefficients using multiple gradient vectors of the squared error, which are computed at different points on the performance surface. First, the proposed algorithm using 2 ADF's is discussed. Simulation results show that both high speed convergence and low final misadjustment can be realized. The computation time of this proposed algorithm is nearly as much as that of LMS if parallel processing techniques are used. Moreover, the proposed algorithm using more than 2 ADF's is discussed. It is understood that if more than 2 ADF's are used, further improvement in the convergence speed in not realized, but a reduction of the final misadjustment and an improvement in the stability are realized. Finally, a method which can improve the convergence property in the presence of correlated input is discussed. It is indicated that using priori knowledge and matrix transformation, the convergence property is quite improved even when a strongly correlated signal input is applied.
ER -