A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification

The performance of the recursive least-squares (RLS) algorithm is governed by the forgetting factor. This parameter leads to a compromise between (1) the tracking capabilities and (2) the misadjustment and stability. In this letter, a variable forgetting factor RLS (VFF-RLS) algorithm is proposed fo...

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Bibliographic Details
Published in:IEEE signal processing letters Vol. 15; pp. 597 - 600
Main Authors: Paleologu, C., Benesty, J., Ciochina, S.
Format: Journal Article
Language:English
Published: New York IEEE 2008
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive filtering
Adaptive filters
Additive noise
Convergence
echo cancellation
Least squares approximation
recursive least-squares (RLS)
Resonance light scattering
Robustness
Signal processing
Signal processing algorithms
Stability
System identification
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary:The performance of the recursive least-squares (RLS) algorithm is governed by the forgetting factor. This parameter leads to a compromise between (1) the tracking capabilities and (2) the misadjustment and stability. In this letter, a variable forgetting factor RLS (VFF-RLS) algorithm is proposed for system identification. In general, the output of the unknown system is corrupted by a noise-like signal. This signal should be recovered in the error signal of the adaptive filter after this one converges to the true solution. This condition is used to control the value of the forgetting factor. The simulation results indicate the good performance and the robustness of the proposed algorithm.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2008.2001559