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Robustness of the filtered-X LMS algorithm— Part II: robustness enhancement by minimal regularization for norm bounded uncertainty

Record type: Article

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 [“Robustness of the Filtered-X LMS Algorithm—Part I: Necessary Conditions for Convergence and the Asymptotic Pseudospectrum of Toeplitz Matrices” 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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Citation

Fraanje, R., Elliott, S.J. and Verhaegen, M. (2007) Robustness of the filtered-X LMS algorithm— Part II: robustness enhancement by minimal regularization for norm bounded uncertainty IEEE Transactions on Signal Processing, 55, (8), pp. 4038-4047. (doi:10.1109/TSP.2007.896086).

More information

Published date: August 2007
Organisations: Signal Processing & Control Group

Identifiers

Local EPrints ID: 49265
URI: http://eprints.soton.ac.uk/id/eprint/49265
ISSN: 1053-587X
PURE UUID: a1bc8cfd-09ff-4ca3-bdb1-28dd79c6f6cc

Catalogue record

Date deposited: 25 Oct 2007
Last modified: 17 Jul 2017 14:56

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