For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Convergence Analysis of Quantizing Method with Correlated Gaussian Data
Kiyoshi TAKAHASHI Noriyoshi KUROYANAGI Shinsaku MORI
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1996/08/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)
NLMS, convergence characteristics, threshold level, correlated gaussian data,
Full Text: PDF>>
In this paper the normalized lease mean square (NLMS) algorithm based on clipping input samples with an arbitrary threshold level is studied. The convergence characteristics of these clipping algorithms with correlated data are presented. In the clipping algorithm, the input samples 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 convergence, the speed of the convergence, and the misadjustment are functions of the threshold level. Furthermore an optimum threshold level is derived in terms of the convergence speed under the condition of the constant misadjustment.