Title :
A Novel Generalization of Modified LMS Algorithm to Fractional Order
Author :
Yun Tan ; ZhiQiang He ; Baoyu Tian
Author_Institution :
Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
In this letter, the modified least mean squares (MLMS) algorithm proposed by Kretschmer is generalized to fractional order α (0 <; α ≤ 1 ). Such generalization is achieved by replacing the first order difference of the weight updating equation with a fractional one. The convergence speed, weight noise and implementation issue of the generalized MLMS (GMLMS) algorithm are examined. It is shown that for smaller step size, the fractional order α functions the same as the step size, which means that a smaller α will give smaller weight noise while a bigger α will give faster convergence speed.
Keywords :
least mean squares methods; GMLMS algorithm; MLMS algorithm; convergence speed; fractional order α functions; generalized modified least mean squares algorithm; step size; weight noise; weight updating equation; Calculus; Convergence; Equations; Indexes; Least squares approximations; Noise; Signal processing algorithms; Discrete Mittag–Leffler function; LMS; fractional difference; fractional order integrator;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2015.2394301