Problems in estimating dynamics from data

Eric Kostelich

doi:10.1016/0167-2789(92)90105-V

Problems in estimating dynamics from data

Eric Kostelich

Mathematical and Statistical Sciences, School of (SoMSS)

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

53 Scopus citations

Abstract

This paper discusses some difficulties in estimating dynamics from time-delay embeddings of experimental data that can be characterized as low-dimensional. A new procedure is described to reduce noise by exploiting the properties of saddle periodic orbits on the reconstructed attractor.

Original language	English (US)
Pages (from-to)	138-152
Number of pages	15
Journal	Physica D: Nonlinear Phenomena
Volume	58
Issue number	1-4
DOIs	https://doi.org/10.1016/0167-2789(92)90105-V
State	Published - Sep 15 1992

ASJC Scopus subject areas

Statistical and Nonlinear Physics
Mathematical Physics
Condensed Matter Physics
Applied Mathematics

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10.1016/0167-2789(92)90105-V

Cite this

@article{627026cf12854633940a61f8b559d9db,

title = "Problems in estimating dynamics from data",

abstract = "This paper discusses some difficulties in estimating dynamics from time-delay embeddings of experimental data that can be characterized as low-dimensional. A new procedure is described to reduce noise by exploiting the properties of saddle periodic orbits on the reconstructed attractor.",

author = "Eric Kostelich",

note = "Funding Information: The author thanks H. Tong and two anonymous referees for helpful comments. This work is supported by the NSF Applied and Computational Mathematics Program under grant No. DMS-9017174.",

year = "1992",

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PY - 1992/9/15

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N2 - This paper discusses some difficulties in estimating dynamics from time-delay embeddings of experimental data that can be characterized as low-dimensional. A new procedure is described to reduce noise by exploiting the properties of saddle periodic orbits on the reconstructed attractor.

AB - This paper discusses some difficulties in estimating dynamics from time-delay embeddings of experimental data that can be characterized as low-dimensional. A new procedure is described to reduce noise by exploiting the properties of saddle periodic orbits on the reconstructed attractor.

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Problems in estimating dynamics from data

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