An efficient least mean squares algorithm based on q-gradient

Ubaid M. Al-Saggaf; Muhammad Moinuddin; Azzedine Zerguine

doi:10.1109/ACSSC.2014.7094580

An efficient least mean squares algorithm based on q-gradient

Ubaid M. Al-Saggaf
, Muhammad Moinuddin
, Azzedine Zerguine

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

9 Scopus citations

Abstract

In this work, we propose a novel LMS type algorithm by utilizing the q-gradient. The concept of q-gradient is derived from the definition of Jacksons derivative which is also called as the q-derivative. The q-gradient based LMS algorithm results in faster convergence for q > 1 because of the fact that the q-derivative, unlike the conventional derivative which evaluates tangent, computes the secant of the cost function and hence takes larger steps towards the optimum solution. We show an important application of the proposed q-LMS algorithm in which it acts like a whitening filter. Convergence analysis of the proposed algorithm is also presented. Simulation results are presented to support our theoretical findings.

Original language	English
Title of host publication	Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	891-894
Number of pages	4
ISBN (Electronic)	9781479982974
DOIs	https://doi.org/10.1109/ACSSC.2014.7094580
State	Published - 24 Apr 2015

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2015-April
ISSN (Print)	1058-6393

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

Convergence analysis
LMS algorithm
q-LMS algorithm
q-gradient

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2014.7094580

Cite this

Al-Saggaf, U. M., Moinuddin, M., & Zerguine, A. (2015). An efficient least mean squares algorithm based on q-gradient. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 891-894). Article 7094580 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094580

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abstract = "In this work, we propose a novel LMS type algorithm by utilizing the q-gradient. The concept of q-gradient is derived from the definition of Jacksons derivative which is also called as the q-derivative. The q-gradient based LMS algorithm results in faster convergence for q > 1 because of the fact that the q-derivative, unlike the conventional derivative which evaluates tangent, computes the secant of the cost function and hence takes larger steps towards the optimum solution. We show an important application of the proposed q-LMS algorithm in which it acts like a whitening filter. Convergence analysis of the proposed algorithm is also presented. Simulation results are presented to support our theoretical findings.",

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doi = "10.1109/ACSSC.2014.7094580",

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Al-Saggaf, UM, Moinuddin, M & Zerguine, A 2015, An efficient least mean squares algorithm based on q-gradient. in MB Matthews (ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers., 7094580, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2015-April, IEEE Computer Society, pp. 891-894. https://doi.org/10.1109/ACSSC.2014.7094580

An efficient least mean squares algorithm based on q-gradient. / Al-Saggaf, Ubaid M.; Moinuddin, Muhammad; Zerguine, Azzedine.
Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. ed. / Michael B. Matthews. IEEE Computer Society, 2015. p. 891-894 7094580 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April).

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

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AB - In this work, we propose a novel LMS type algorithm by utilizing the q-gradient. The concept of q-gradient is derived from the definition of Jacksons derivative which is also called as the q-derivative. The q-gradient based LMS algorithm results in faster convergence for q > 1 because of the fact that the q-derivative, unlike the conventional derivative which evaluates tangent, computes the secant of the cost function and hence takes larger steps towards the optimum solution. We show an important application of the proposed q-LMS algorithm in which it acts like a whitening filter. Convergence analysis of the proposed algorithm is also presented. Simulation results are presented to support our theoretical findings.

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An efficient least mean squares algorithm based on q-gradient

Abstract

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Keywords

ASJC Scopus subject areas

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