Kernel least mean square with single feedback

Ji Zhao, Xiaofeng Liao, Shiyuan Wang, Chi Kong Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

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.
Original languageEnglish
Article number6977884
Pages (from-to)953-957
Number of pages5
JournalIEEE Signal Processing Letters
Volume22
Issue number7
DOIs
Publication statusPublished - 1 Jul 2015

Keywords

  • Kernel adaptive filter
  • KLMS
  • recurrent fashion
  • single feedback

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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