A new and better prediction model for chaotic time series based on ESN and PCA

Yangming Guo, Jiangyan Sun, Linjuan Fu, Zhengjun Zhai

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The introduction of the full paper points out that the prediction model based on ESN (echo state network) proposed by H. Jaeger in Ref. 1 is, in our opinion, not good enough. So we propose what we believe to be a new and better prediction model based on ESN and PCA (principal component analysis). Section 1 briefs the relevant information in Refs. 2 through 6. Section 2 explains our prediction model; its block diagram is given in Fig. 2. Section 3 gives eq. (6) for measuring the prediction precision of our prediction model. Section 4 compares the simulation results of our prediction model with those of the prediction model based on ESN. The simulation results, given in Figs. 4 through 7 and in Tables 1, 2 and 3, and their analysis show preliminarily that: (1) the computing complexity of our prediction model is lower; (2) its training time is shorter; (3)the prediction rate is higher than that of the prediction model based on ESN.

Original languageEnglish
Pages (from-to)946-951
Number of pages6
JournalXibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
Volume28
Issue number6
StatePublished - Dec 2010

Keywords

  • Chaotic time series
  • Echo state network(ESN)
  • Prediction model
  • Principal component analysis

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