Open AccessBias‐compensated affine‐projection‐like algorithms with noisy input
Author(s) -
Zhao Haiquan,
Zheng Zongsheng
Publication year - 2016
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.2015.3550
Subject(s) - affine transformation , algorithm , noise (video) , context (archaeology) , variance (accounting) , projection (relational algebra) , compensation (psychology) , computer science , noise measurement , identification (biology) , orthographic projection , affine shape adaptation , mathematics , affine combination , artificial intelligence , noise reduction , psychology , paleontology , botany , accounting , psychoanalysis , pure mathematics , business , image (mathematics) , biology
A new class of bias‐compensated affine‐projection‐like (APL) algorithms is proposed, in which a bias‐compensation vector is derived to eliminate the bias caused by the noisy input. In addition, a new estimation method for the input noise variance is proposed which does not require the input–output noise variance ratio in advance. Simulations in a system identification context show that the proposed algorithms achieve significant improvements in steady‐state misalignment as compared with the conventional APL algorithms.