Abstract
One of the most recent modifications on Widrow and Hoff's LMS algorithm has been the inclusion of a momentum term into the weight update equation. The resulting algorithm is referred to as “The Momentum LMS (MLMS) algorithm”. This paper revises the basic properties of the MLMS algorithm for stationary inputs. As a result, new bounds, on the parameters of the algorithm, for convergence are found, and it is shown that, under slow convergence conditions, this new algorithm is equivalent to the usual LMS algorithm, but it outperforms the LMS algorithm for fast convergence cases and for inputs containing inpulsive noise components.
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