On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms

T. Y. Al-Naffouri; A. H. Sayed; T. Kailath

doi:10.1109/ICASSP.2000.862014

On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms

T. Y. Al-Naffouri, A. H. Sayed, T. Kailath

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

This paper derives an expression for the optimal error nonlinearity in adaptive filter design. Using an energy conservation relation, and some typical assumptions, the choice of the error function is optimized by minimizing the mean-square deviation subject to a fixed rate of convergence. The resulting optimal choice is shown to subsume earlier results as special cases.

Original language	English (US)
Title of host publication	Signal Processing Theory and Methods I
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	464-467
Number of pages	4
ISBN (Electronic)	0780362934
DOIs	https://doi.org/10.1109/ICASSP.2000.862014
State	Published - 2000
Externally published	Yes
Event	25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey Duration: Jun 5 2000 → Jun 9 2000

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	1
ISSN (Print)	1520-6149

Other

Other	25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000
Country/Territory	Turkey
City	Istanbul
Period	06/5/00 → 06/9/00

Bibliographical note

Publisher Copyright:
© 2000 IEEE.

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICASSP.2000.862014

Cite this

Al-Naffouri, T. Y., Sayed, A. H., & Kailath, T. (2000). On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms. In Signal Processing Theory and Methods I (pp. 464-467). Article 862014 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2000.862014

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title = "On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms",

abstract = "This paper derives an expression for the optimal error nonlinearity in adaptive filter design. Using an energy conservation relation, and some typical assumptions, the choice of the error function is optimized by minimizing the mean-square deviation subject to a fixed rate of convergence. The resulting optimal choice is shown to subsume earlier results as special cases.",

author = "Al-Naffouri, {T. Y.} and Sayed, {A. H.} and T. Kailath",

note = "Publisher Copyright: {\textcopyright} 2000 IEEE.; 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 ; Conference date: 05-06-2000 Through 09-06-2000",

year = "2000",

doi = "10.1109/ICASSP.2000.862014",

language = "English (US)",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Al-Naffouri, TY, Sayed, AH & Kailath, T 2000, On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms. in Signal Processing Theory and Methods I., 862014, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 464-467, 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000, Istanbul, Turkey, 06/5/00. https://doi.org/10.1109/ICASSP.2000.862014

On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms. / Al-Naffouri, T. Y.; Sayed, A. H.; Kailath, T.
Signal Processing Theory and Methods I. Institute of Electrical and Electronics Engineers Inc., 2000. p. 464-467 862014 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms

AU - Al-Naffouri, T. Y.

AU - Sayed, A. H.

AU - Kailath, T.

PY - 2000

Y1 - 2000

N2 - This paper derives an expression for the optimal error nonlinearity in adaptive filter design. Using an energy conservation relation, and some typical assumptions, the choice of the error function is optimized by minimizing the mean-square deviation subject to a fixed rate of convergence. The resulting optimal choice is shown to subsume earlier results as special cases.

AB - This paper derives an expression for the optimal error nonlinearity in adaptive filter design. Using an energy conservation relation, and some typical assumptions, the choice of the error function is optimized by minimizing the mean-square deviation subject to a fixed rate of convergence. The resulting optimal choice is shown to subsume earlier results as special cases.

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DO - 10.1109/ICASSP.2000.862014

M3 - Conference contribution

AN - SCOPUS:0033708751

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 464

EP - 467

BT - Signal Processing Theory and Methods I

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000

Y2 - 5 June 2000 through 9 June 2000

ER -

Al-Naffouri TY, Sayed AH, Kailath T. On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms. In Signal Processing Theory and Methods I. Institute of Electrical and Electronics Engineers Inc. 2000. p. 464-467. 862014. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2000.862014

On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms

Abstract

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Bibliographical note

ASJC Scopus subject areas

UN SDGs

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