Particle-filter-based estimation and prediction of chaotic states

Bai Zhang; Maoyin Chen; Donghua Zhou; Zhengxi Li

doi:10.1016/j.chaos.2005.11.098

Particle-filter-based estimation and prediction of chaotic states

Bai Zhang, Maoyin Chen, Donghua Zhou, Zhengxi Li

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

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 language	English (US)
Pages (from-to)	1491-1498
Number of pages	8
Journal	Chaos, Solitons and Fractals
Volume	32
Issue number	4
DOIs	https://doi.org/10.1016/j.chaos.2005.11.098
State	Published - May 2007
Externally published	Yes

ASJC Scopus subject areas

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

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10.1016/j.chaos.2005.11.098

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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.",

author = "Bai Zhang and Maoyin Chen and Donghua Zhou and Zhengxi Li",

note = "Funding Information: This work was mainly supported by NSFC (Grant No. 60234010), partially supported by the Field Bus Technology & Automation Key Lab of Beijing at North China and the national 973 program (Grant No. 2002CB312200) of China.",

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AU - Chen, Maoyin

AU - Zhou, Donghua

AU - Li, Zhengxi

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PY - 2007/5

Y1 - 2007/5

N2 - 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.

AB - 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.

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JO - Chaos, Solitons and Fractals

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ER -

Particle-filter-based estimation and prediction of chaotic states

Abstract

ASJC Scopus subject areas

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