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Adaptive system identification using robust LMS/F algorithm
Author(s) -
Gui Guan,
Peng Wei,
Adachi Fumiyuki
Publication year - 2014
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.2517
Subject(s) - computer science , least mean squares filter , computational complexity theory , algorithm , identification (biology) , system identification , adaptive filter , signal to noise ratio (imaging) , noise (video) , artificial intelligence , telecommunications , data mining , botany , image (mathematics) , biology , measure (data warehouse)
Adaptive system identification (ASI) problems have attracted both academic and industrial attentions for a long time. As one of the classical approaches for ASI, performance of least mean square (LMS) is unstable in low signal‐to‐noise ratio (SNR) region. On the contrary, least mean fourth (LMF) algorithm is difficult to implement in practical system because of its high computational complexity in high SNR region, and hence it is usually neglected by researchers. In this paper, we propose an effective approach to identify unknown system adaptively by using combined LMS and LMF algorithms in different SNR regions. Experiment‐based parameter selection is established to optimize the performance as well as to keep the low computational complexity. Copyright © 2013 John Wiley & Sons, Ltd.