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Variable Step-Size Least Mean Fourth Adaptive Algorithm (Paperback)
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Variable Step-Size Least Mean Fourth Adaptive Algorithm (Paperback)
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Adaptive ¬ltering algorithms have been proposed to improve the
performance in terms of their steady-state error and convergence
rate. They have also been developed to counter the e¬ects of
measurement noise characterised as white Gaussian. The Least Mean
Square (LMS) algorithm has been by far the most important in terms
of its simplicity and range of applications. Thus it has been a
centre point of extensive research. In particular, the time-varying
step-size models of LMS have contributed immensely in the
performance enhancement of LMS algorithm. By comparison the Least
Mean Fourth (LMF) algorithm that gives better convergence rate in
non-Gaussian noise environment has not been researched in its full
spectrum. The aim of this work is to exploit the research in LMS
algorithm particularly in the model of time-varying step-size
parameter and extend it to the LMF. We have developed a variable
step-size LMF algorithm with the aim to achieve better performance
than the traditional LMF and retain its inherent dominance over the
LMF in non-Gaussian noise environments.
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