Variable p norm constrained LMS algorithm based on gradient of root relative deviation.pdf
Systems and Control
2016-03-31 v1
Abstract
A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy of varying p is presented, which is applied to system identification in this letter. The parameter p is iteratively adjusted by the gradient method applied to the root relative deviation of the estimated weight vector. Numerical simulations show that this new algorithm achieves lower steady-state error as well as equally fast convergence compared with the traditional Lp-LMS and LMS algorithms in the application setting of sparse system identification in the presence of noise.
Cite
@article{arxiv.1603.09022,
title = {Variable p norm constrained LMS algorithm based on gradient of root relative deviation.pdf},
author = {Yong Feng and Fei Chen and Jiasong Wu},
journal= {arXiv preprint arXiv:1603.09022},
year = {2016}
}
Comments
2 pages, 3 figures, 1 table, 9 equations