Statistical convergence of the adaptive least mean fourth algorithm

Author

Cho, Sung Ho ; Kim, Sang Deok ; Jeon, Ki Young

Author_Institution

Dept. of Electron. Eng., Hanyang Univ., Ansan, South Korea

Volume

1

fYear

1996

fDate

14-18 Oct 1996

Firstpage

610

Abstract

This paper presents a statistical convergence analysis of the adaptive least mean fourth (LMF) algorithm that minimizes the estimation error in the mean fourth sense. A set of nonlinear evolution equations for the mean and mean-squared behavior of the algorithm is derived. A condition for the mean convergence is also found, and it turns out that the convergence of the LMF algorithm strongly depends on the choice of initial conditions. Through the extensive computer simulations, we have observed that there are many cases in which the LMF algorithm outperforms the LMS algorithm from the viewpoints of the convergence speed as well as the precision of adaptations

Keywords

Gaussian processes; adaptive filters; adaptive signal processing; convergence of numerical methods; error analysis; filtering theory; identification; least mean squares methods; nonlinear equations; statistical analysis; Gaussian signals; LMF algorithm; adaptation precision; adaptive filter; adaptive least mean fourth algorithm; adaptive system identification model; computer simulations; convergence speed; estimation error minimisation; initial condition; mean; mean convergence; mean-squared behavior; nonlinear evolution equations; statistical convergence analysis; Adaptive filters; Algorithm design and analysis; Convergence; Estimation error; Filtering algorithms; Least squares approximation; Nonlinear equations; Signal processing; Transient analysis; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 1996., 3rd International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-2912-0

Type

conf

DOI

10.1109/ICSIGP.1996.567338

Filename

567338