Fraanje, R., Elliott, S.J. and Verhaegen, M.
Robustness of the filtered-x LMS algorithm: part 11: robustness enhancement by minimal regularization for norm bounded uncertainty
IEEE Transactions on Signal Processing, 55, (8), . (doi:10.1109/TSP.2007.896086).
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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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