Title :
Bursting in the LMS algorithm
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA
fDate :
10/1/1995 12:00:00 AM
Abstract :
The least mean square (LMS) algorithm is known to converge in the mean and in the mean square. However, during short time periods, the error sequence can blow up and cause severe disturbances, especially for non-Gaussian processes. The paper discusses potential short time unstable behavior of the LMS algorithm for spherically invariant random processes (SIRP) like Gaussian, Laplacian, and K0. The result of this investigation is that the probability for bursting decreases with the step size. However, since a smaller step size also causes a slower convergence rate, one has to choose a tradeoff between convergence speed and the frequence of bursting
Keywords :
Gaussian processes; Laplace equations; error analysis; least mean squares methods; numerical stability; probability; random processes; sequences; signal processing; Gaussian process; K0 process; LMS algorithm; Laplacian process; bursting; convergence rate; disturbances; error sequence; least mean squares; nonGaussian processes; short time unstable behavior; spherically invariant random processes; step size; Algorithm design and analysis; Convergence; Filters; Frequency; Laplace equations; Least squares approximation; Probability; Random processes; Signal processing algorithms; Statistical analysis;
Journal_Title :
Signal Processing, IEEE Transactions on
