Steady-state performance of non-negative least-mean-square algorithm and its variants
Machine Learning
2015-06-18 v1
Abstract
Non-negative least-mean-square (NNLMS) algorithm and its variants have been proposed for online estimation under non-negativity constraints. The transient behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS algorithms have been studied in our previous work. In this technical report, we derive closed-form expressions for the steady-state excess mean-square error (EMSE) for the four algorithms. Simulations results illustrate the accuracy of the theoretical results. This is a complementary material to our previous work.
Keywords
Cite
@article{arxiv.1401.6376,
title = {Steady-state performance of non-negative least-mean-square algorithm and its variants},
author = {Jie Chen and José Carlos M. Bermudez and Cédric Richard},
journal= {arXiv preprint arXiv:1401.6376},
year = {2015}
}
Comments
10 pages, 4 figures