Abstract
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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call