Open AccessBias‐compensated robust set‐membership NLMS algorithm against impulsive noises and noisy inputs
Author(s) -
Zheng Zongsheng,
Liu Zhigang,
Lu Lu
Publication year - 2017
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.2017.1812
Subject(s) - robustness (evolution) , algorithm , noise (video) , mean squared error , computer science , control theory (sociology) , context (archaeology) , mathematics , statistics , artificial intelligence , control (management) , image (mathematics) , paleontology , biochemistry , chemistry , biology , gene
By minimising a new cost function that contains robust set‐membership error bound, a bias‐compensated robust set‐membership normalised least mean square (NLMS) algorithm is proposed, which is characterised by its robustness against impulsive noises and noisy inputs. To estimate the input noise variance in impulsive noise environments, a new estimation method is proposed in which there is no need to know the input–output noise variance ratio in advance. Simulations in a system identification context demonstrate that the proposed algorithm achieves improved robustness and better performance than the existing algorithms.