Kernel RLS Algorithm Using Variable Forgetting Factor 


Vol. 40,  No. 9, pp. 1793-1801, Sep.  2015


PDF
  Abstract

In a recent work, kernel recursive least-squares tracker (KRLS-T) algorithm has been proposed. It is capable of tracking in non-stationary environments using a forgetting mechanism built on a Bayesian framework. The forgetting mechanism in KRLS-T is implemented by a fixed forgetting factor. In practice, however, we frequently meet that the fixed forgetting factor cannot handle time-varying system effectively. In this paper we propose a new KRLS-T with a variable forgetting factor. Experimental results show that proposed algorithm can handle time-varying system more effectively than the KRLS-T.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

J. Lim and Y. Pyeon, "Kernel RLS Algorithm Using Variable Forgetting Factor," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 9, pp. 1793-1801, 2015. DOI: .

[ACM Style]

Jun-Seok Lim and Yong-Guk Pyeon. 2015. Kernel RLS Algorithm Using Variable Forgetting Factor. The Journal of Korean Institute of Communications and Information Sciences, 40, 9, (2015), 1793-1801. DOI: .

[KICS Style]

Jun-Seok Lim and Yong-Guk Pyeon, "Kernel RLS Algorithm Using Variable Forgetting Factor," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 9, pp. 1793-1801, 9. 2015.

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880