Open AccessBias‐compensated normalised LMS algorithm with noisy input
Author(s) -
Kang B.,
Yoo J.,
Park P.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2013.0246
Subject(s) - computer science , algorithm , control theory (sociology) , mathematics , artificial intelligence , control (management)
A new bias‐compensated normalised least mean square (NLMS) algorithm for parameter estimation with a noisy input is proposed. The algorithm is obtained from an approximated cost function based on the statistical properties of the input noise and involves a condition checking constraint to decide whether the weight coefficient vector must be updated. Simulation results show that the proposed algorithm is more robust and accurate than the conventional method.