NDLI: The Averaged, Overdetermined, and Generalized LMS Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Alameda-Hernandez, E. Blanco, D. Ruiz, D.P. Carrion, M.C.
Copyright Year	1991
Abstract	This paper provides and exploits one possible formal framework in which to compare and contrast the two most important families of adaptive algorithms: the least-mean square (LMS) family and the recursive least squares (RLS) family. Existing and well-known algorithms, belonging to any of these two families, like the LMS algorithm and the RLS algorithm, have a natural position within the proposed formal framework. The proposed formal framework also includes - among others - the LMS/overdetermined recursive instrumental variable (ORIV) algorithm and the generalized LMS (GLMS) algorithm, which is an instrumental variable (IV) enable LMS algorithm. Furthermore, this formal framework allows a straightforward derivation of new algorithms, with enhanced properties respect to the existing ones: specifically, the ORIV algorithm is exported to the LMS family, resulting in the derivation of the averaged, overdetermined, and generalized LMS (AOGLMS) algorithm, an overdetermined LMS algorithm able to work with an IV. The proposed AOGLMS algorithm overcomes - as we analytically show here - the limitations of GLMS and possesses a much lower computational burden than LMS/ORIV, being in this way a better alternative to both algorithms. Simulations verify the analysis.
Sponsorship	IEEE Signal Processing Society
Starting Page	5593
Ending Page	5603
Page Count	11
File Size	705198
File Format	PDF
ISSN	1053587X
Volume Number	55
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares approximation Signal processing algorithms Least squares methods Resonance light scattering Instruments Adaptive algorithm Stochastic processes Adaptive filters Statistics Eigenvalues and eigenfunctions stochastic gradient Instrumental variable least squares orthogonality conditions recursive algorithms
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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