Noise robust estimates of correlation dimension and K2 entropy

G. Nolte; A. Ziehe; K. R. Müller

Noise robust estimates of correlation dimension and K₂ entropy

G. Nolte, A. Ziehe, K. R. Müller

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

Abstract

A Gaussian kernel based method for reducing the noise bias in estimates of correlation dimension and K₂ entropy of dynamical system attractors is presented. The method is local in scale space and requires for each scale, only the knowledge of the function △. The performance of the method is demonstrated for various examples using data from the Hénon map and the Lorenz and Rôssler system.

Original language	English
Pages (from-to)	016112/1-016112/10
Journal	Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume	64
Issue number	1 II
Publication status	Published - 2001 Jul
Externally published	Yes

ASJC Scopus subject areas

Statistical and Nonlinear Physics
Statistics and Probability
Condensed Matter Physics

Cite this

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title = "Noise robust estimates of correlation dimension and K2 entropy",

abstract = "A Gaussian kernel based method for reducing the noise bias in estimates of correlation dimension and K2 entropy of dynamical system attractors is presented. The method is local in scale space and requires for each scale, only the knowledge of the function △. The performance of the method is demonstrated for various examples using data from the H{\'e}non map and the Lorenz and R{\^o}ssler system.",

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AU - Ziehe, A.

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N2 - A Gaussian kernel based method for reducing the noise bias in estimates of correlation dimension and K2 entropy of dynamical system attractors is presented. The method is local in scale space and requires for each scale, only the knowledge of the function △. The performance of the method is demonstrated for various examples using data from the Hénon map and the Lorenz and Rôssler system.

AB - A Gaussian kernel based method for reducing the noise bias in estimates of correlation dimension and K2 entropy of dynamical system attractors is presented. The method is local in scale space and requires for each scale, only the knowledge of the function △. The performance of the method is demonstrated for various examples using data from the Hénon map and the Lorenz and Rôssler system.

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SN - 1539-3755

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Noise robust estimates of correlation dimension and K₂ entropy

Abstract

ASJC Scopus subject areas

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