CONVERGENCE ANALYSIS OF QUANTIZING METHOD WITH CORRELATED GAUSSIAN DATA

In this paper the normalized least mean square (NLMS) algorithm based on clipping input samples with an arbitrary threshold level is studied . The convergence characteristics of these clipping algorithms with co rrelated data are presented. In the clipping algorithm, the input samp les are clipped only when the input samples are greater than or equal to the threshold level and otherwise the input samples are set to zero . The results of the analysis yield that the gain constant to ensure c onvergence, the speed of the convergence, and the misadjustment are fu nctions of the threshold level. Furthermore an optimum threshold level is derived in terms of the convergence speed under the condition of t he constant misadjustment.