A stochastic gradient adaptive filter with gradient adaptive step size

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Publication Type Journal Article
School or College College of Engineering
Department Electrical & Computer Engineering
Creator Mathews, V. John
Other Author Xie, Zhenhua
Title A stochastic gradient adaptive filter with gradient adaptive step size
Date 1993
Description Abstract-This paper presents an adaptive step-size gradient adaptive filter. The step size of the adaptive filter is changed according to a gradient descent algorithm designed to reduce the squared estimation error during each iteration. An approximate analysis of the performance of the adaptive filter when its inputs are zero mean, white, and Gaussian and the set of optimal coefficients are time varying according to a random walk model is presented in the paper. The algorithm has very good convergence speed and low steady-state misadjustment. Furthermore, the tracking performance of these algorithms in nonstationary environments is relatively insensitive to the choice of the parameters of the adaptive filter and is very close to the best possible performance of the least mean square (LMS) algorithm for a large range of values of the step size of the step size adaptation algorithm. Several simulation examples demonstrating the good properties of the adaptive filter as well as verifying the analytical results are also presented in the paper.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 41
Issue 6
First Page 2075
Last Page 2087
Language eng
Bibliographic Citation Mathews, V. J., & Xie, Z. (1993). A stochastic gradient adaptive filter with gradient adaptive step size. IEEE Transactions on Signal Processing, 41(6), 2075-87. June.
Rights Management (c) 1993 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 1,043,260 bytes
Identifier ir-main,15080
ARK ark:/87278/s6d22g1x
Setname ir_uspace
Date Created 2012-06-13
Date Modified 2021-05-06
ID 704951
Reference URL https://collections.lib.utah.edu/ark:/87278/s6d22g1x
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