Title :
Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data
Author :
Tarrab, Moshe ; Feuer, Arie
Author_Institution :
Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel
fDate :
7/1/1988 12:00:00 AM
Abstract :
It is demonstrated that the normalized least mean square (NLMS) algorithm can be viewed as a modification of the widely used LMS algorithm. The NLMS is shown to have an important advantage over the LMS, which is that its convergence is independent of environmental changes. In addition, the authors present a comprehensive study of the first and second-order behavior in the NLMS algorithm. They show that the NLMS algorithm exhibits significant improvement over the LMS algorithm in convergence rate, while its steady-state performance is considerably worse
Keywords :
convergence of numerical methods; information theory; least squares approximations; NLMS algorithm; convergence; first order behavior; least mean square algorithm; normalized LMS algorithm; performance analysis; second-order behavior; uncorrelated Gaussian data; Adaptive algorithm; Algorithm design and analysis; Convergence; Helium; Least squares approximation; Performance analysis; Performance gain; Statistics; Steady-state; Stochastic processes;
Journal_Title :
Information Theory, IEEE Transactions on
