Abstract
The well known least mean square (LMS) algorithm, or variations thereof, are frequently used in adaptive systems. When the LMS algorithm is implemented in a finite precision environment, it suffers from quantization effects. These effects can severely degrade the performance of the algorithm. This paper proposes a modification of the LMS algorithm that reduces the impact of quantization at virtually no extra computational cost. The paper contains an off-line evaluation of a system identification scheme where the presented algorithm outperforms the classical LMS algorithm yielding a better modelling of the unknown plant. This approach is well suited for adaptive system identification, e.g. beamforming, electrocardiography, and echo cancelling.
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