Kernel Least Mean Square with Single Feedback

Author

Ji Zhao ; Xiaofeng Liao ; Shiyuan Wang ; Tse, Chi K.

Author_Institution

Sch. of Electron. & Inf. Eng., Southwest Univ., Chongqing, China

Volume

22

Issue

7

fYear

2015

fDate

Jul-15

Firstpage

953

Lastpage

957

Abstract

In this letter, a novel kernel adaptive filtering algorithm, namely the kernel least mean square with single feedback (SF-KLMS) algorithm, is proposed. In SF-KLMS, only a single delayed output is used to update the weights in a recurrent fashion. The use of past information accelerates the convergence rate significantly. Compared with the kernel adaptive filter using multiple feedback, SF-KLMS has a more compact and efficient structure. Simulations in the context of time-series prediction and nonlinear regression show that SF-KLMS outperforms not only the kernel adaptive filter with multiple feedback but also the kernel adaptive filter without feedback in terms of convergence rate and mean square error.

Keywords

adaptive filters; convergence; feedback; mean square error methods; nonlinear filters; regression analysis; time series; SF-KLMS algorithm; convergence rate; kernel adaptive filtering algorithm; kernel least mean square; mean square error; nonlinear regression; single feedback; time-series prediction; Algorithm design and analysis; Convergence; Kernel; Least squares approximations; Prediction algorithms; Signal processing algorithms; Testing; KLMS; Kernel adaptive filter; recurrent fashion; single feedback;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2014.2377726

Filename

6977884