A modified quantized kernel least mean square algorithm for prediction of chaotic time series

Yunfei Zheng, Shiyuan Wang, Jiuchao Feng, Chi Kong Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)


A modified quantized kernel least mean square (M-QKLMS) algorithm is proposed in this paper, which is an improvement of quantized kernel least mean square (QKLMS) and the gradient descent method is used to update the coefficient of filter. Unlike the QKLMS method which only considers the prediction error, the M-QKLMS method uses both the new training data and the prediction error for coefficient adjustment of the closest center in the dictionary. Therefore, the proposed method completely utilizes the knowledge hidden in the new training data, and achieves a better accuracy. In addition, the energy conservation relation and a sufficient condition for mean-square convergence of the proposed method are obtained. Simulations on prediction of chaotic time series show that the M-QKLMS method outperforms the QKLMS method in terms of steady-state mean square errors.
Original languageEnglish
Pages (from-to)130-136
Number of pages7
JournalDigital Signal Processing: A Review Journal
Publication statusPublished - 1 Jan 2016


  • Chaotic time series
  • Coefficient update
  • Gradient descent method
  • Quantized kernel least-mean-square

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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