Regularized kernel least mean square algorithm with multiple-delay feedback

Shiyuan Wang, Yunfei Zheng, Chengxiu Ling

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

In the design of adaptive filters, feedback can be utilized to improve the convergence rate and filtering accuracy. This letter introduces a feedback structure with multiple delay to design kernel adaptive filters. A regularized loss function is minimized by using the steepest descent method. The past information of output is therefore reused to update the filter weights in a recurrent fashion, resulting in a novel regularized kernel least mean square algorithm with multiple-delay feedback (RKLMS-MDF). Compared with other kernel adaptive filters with or without feedback, RKLMS-MDF can improve the filtering performance from the respects of the convergence rate and the steady-state mean square error.

Original languageEnglish
Article number23899
Pages (from-to)98-101
Number of pages4
JournalIEEE Signal Processing Letters
Volume23
Issue number1
DOIs
Publication statusPublished - Jan 2016
Externally publishedYes

Keywords

  • Kernel adaptive filters
  • Multiple-delay feedback
  • Recurrent fashion
  • Sparsification

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