Robustness of the Filtered-X LMS Algorithm- Part II: Robustness Enhancement by Minimal Regularization for Norm Bounded Uncertainty
The relationship between the regularization methods proposed in the literature to increase the robustness of the filtered-X LMS (FXLMS) algorithm is discussed. It is shown that the existing methods are special cases of a more general robust FXLMS algorithm in which particular filters determine the t...
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Veröffentlicht in: | IEEE transactions on signal processing 2007-08, Vol.55 (8), p.4038-4047 |
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Sprache: | eng |
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Zusammenfassung: | The relationship between the regularization methods proposed in the literature to increase the robustness of the filtered-X LMS (FXLMS) algorithm is discussed. It is shown that the existing methods are special cases of a more general robust FXLMS algorithm in which particular filters determine the type of regularization. Based on the analysis by Fraanje, Verhaegen, and Elliott [ldquorobustness of the filtered-X LMS algorithm - part I: necessary conditions for convergence and the asymptotic pseudospectrum of Toeplitz Matricesrdquo of this issue], regularization filters are designed that guarantee that the strictly positive real conditions for asymptotic convergence or noncritical behavior are just satisfied for all uncertain systems contained in a particular norm bounded set. |
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ISSN: | 1053-587X 1941-0476 |
DOI: | 10.1109/TSP.2007.896086 |