Improved data-selective LMS-Newton adaptation algorithms

Authors:
Md. Zulfiquar Ali Bhotto
Affiliations:
Department of Electrical and Computer Engineering, University of Victoria, BC, Canada
Venue:
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Year:
2009

Citing 2
Cited 0

Adaptive Filtering: Algorithms and Practical Implementation

Adaptive Filtering: Algorithms and Practical Implementation
Analysis of LMS-Newton adaptive filtering algorithms with variableconvergence factor

IEEE Transactions on Signal Processing

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Abstract

Improved versions of two known LMSN algorithms are proposed. In these algorithms, data-selective weight adaptation is performed and in this way reduced steady-state misalignment is achieved relative to that in the known LMSN algorithms while requiring a similar number of iterations to converge. On the other hand, for a constant misalignment a significant reduction in the convergence speed can be achieved. In addition, the modified algorithms require a reduced number of updates, which leads to a reduced amount of computation relative to that required by the known LMSN algorithms.