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A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering
Author(s) -
Benallal A.,
Arezki M.
Publication year - 2014
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2423
Subject(s) - convergence (economics) , algorithm , adaptive filter , mathematics , square (algebra) , mean squared error , type (biology) , mean square , computer science , statistics , economics , ecology , geometry , biology , economic growth
SUMMARY A new adaptive algorithm with fast convergence and low complexity is presented. By using the calculation structure of the dual Kalman variables of the fast transversal filter algorithm and a simple decorrelating technique for the input signal, we obtain an algorithm that exhibits faster convergence speed and enhanced tracking ability compared with the normalized least‐mean‐square algorithm with similar computational complexity. Copyright © 2013 John Wiley & Sons, Ltd.