Prediction of chaotic time series based on EMD method

Yong Feng Yang, Xing Min Ren, Wei Yang Qin, Ya Feng Wu, Xi Zhe Zhi

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

In order to improve the nonlinear response prediction precision in a long period, the empirical mode decomposition (EMD) method is introduced in the nonlinear prediction. Here, the EMD method is used to decompose the signal, the rosenstein method is used to calculate the largest Lyapunov exponent (LLE), and then the prediction results are obtained on the basis of the LLE. The simulation results of Duffing equation, Lorenz system and cracked rotor system show that the EMD's signals have smaller LLE than the original signal. In this way, the maximum prediction time of a nonlinear signal can be obtained.

Original languageEnglish
Pages (from-to)6139-6144
Number of pages6
JournalWuli Xuebao/Acta Physica Sinica
Volume57
Issue number10
StatePublished - Oct 2008

Keywords

  • Chaos
  • EMD
  • Prediction

Fingerprint

Dive into the research topics of 'Prediction of chaotic time series based on EMD method'. Together they form a unique fingerprint.

Cite this