An improved recursive least squares algorithm robust to input power variation

This paper proposes a new recursive least-squares adaptive algorithm that improves the steady-state performance of the recently proposed variable memory length (VML) algorithm. Most RLS-type algorithms tend to increase the error in the estimated weight vector during reduced power situations. Like VM...

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Bibliographic Details
Published in:IEEE/SP 13th Workshop on Statistical Signal Processing, 2005 pp. 137 - 140
Main Authors: Ludovico, C.S., Bermudez, J.C.M.
Format: Conference Proceeding
Language:English
Published: IEEE 2005
Subjects:
Adaptive algorithm
Adaptive control
Automotive engineering
Convergence
Fault detection
Least squares methods
Robustness
Speech
Steady-state
System identification
ISBN:9780780394032, 0780394038
ISSN:2373-0803
Online Access:Get full text
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Summary:This paper proposes a new recursive least-squares adaptive algorithm that improves the steady-state performance of the recently proposed variable memory length (VML) algorithm. Most RLS-type algorithms tend to increase the error in the estimated weight vector during reduced power situations. Like VML, the new algorithm, called robust VML (RVML), is robust in system identification applications in which the input power is significantly reduced during operation. The RVML algorithm, however, improves the robustness of the VML algorithm when there is significant input power variations during convergence. It should encounter application in systems such as automotive suspension fault detection and adaptive control, and system identification using speech signals. In both cases, considerable periods of power variation during operation are common
ISBN:9780780394032
0780394038
ISSN:2373-0803
DOI:10.1109/SSP.2005.1628579