Particle-filter-based estimation and prediction of chaotic states

Bai Zhang, Maoyin Chen, Donghua Zhou, Zhengxi Li

Research output: Contribution to journalArticlepeer-review

Abstract

This paper considers the problem of estimation and prediction of chaotic states from arbitrarily nonlinear time series. The basic idea is to use a modified particle filter algorithm to deal with the colored or non-Gaussian noise in chaotic states, the unknown input in chaotic maps, and the nonlinearity in time series. Numerical simulations of Holmes map verify our main results.

Original languageEnglish (US)
Pages (from-to)1491-1498
Number of pages8
JournalChaos, Solitons and Fractals
Volume32
Issue number4
DOIs
StatePublished - May 2007

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematics(all)
  • Physics and Astronomy(all)
  • Applied Mathematics

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