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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Veröffentlicht in:	IEEE signal processing letters Jg. 15; S. 597 - 600
Hauptverfasser:	Paleologu, C., Benesty, J., Ciochina, S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Paleologu, C. Benesty, J. Ciochina, S.
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SubjectTerms	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
Title	A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification
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