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Reconstruction of stochastic nonlinear dynamical models from trajectory measurements

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Reconstruction of stochastic nonlinear dynamical models from trajectory measurements. / Smelyanskiy, V. N.; Luchinsky, Dmitry G.; Timucin, D. A. et al.
In: Physical Review E, Vol. 72, No. 2, 08.2005, p. 026202.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Smelyanskiy, VN, Luchinsky, DG, Timucin, DA & Bandrivskyy, A 2005, 'Reconstruction of stochastic nonlinear dynamical models from trajectory measurements', Physical Review E, vol. 72, no. 2, pp. 026202. https://doi.org/10.1103/PhysRevE.72.026202

APA

Smelyanskiy, V. N., Luchinsky, D. G., Timucin, D. A., & Bandrivskyy, A. (2005). Reconstruction of stochastic nonlinear dynamical models from trajectory measurements. Physical Review E, 72(2), 026202. https://doi.org/10.1103/PhysRevE.72.026202

Vancouver

Smelyanskiy VN, Luchinsky DG, Timucin DA, Bandrivskyy A. Reconstruction of stochastic nonlinear dynamical models from trajectory measurements. Physical Review E. 2005 Aug;72(2):026202. doi: 10.1103/PhysRevE.72.026202

Author

Smelyanskiy, V. N. ; Luchinsky, Dmitry G. ; Timucin, D. A. et al. / Reconstruction of stochastic nonlinear dynamical models from trajectory measurements. In: Physical Review E. 2005 ; Vol. 72, No. 2. pp. 026202.

Bibtex

@article{5cc09a4e2e6a4d4e91b45430d88e2b0a,
title = "Reconstruction of stochastic nonlinear dynamical models from trajectory measurements",
abstract = "An algorithm is presented for reconstructing stochastic nonlinear dynamical models from noisy time-series data. The approach is analytical; consequently, the resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. The strengths of the algorithm are illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. The efficiency and accuracy of the algorithm are further demonstrated by inferring a model for a system of five globally and locally coupled noisy oscillators.",
keywords = "stochastic processes, time series, nonlinear dynamical systems, noise, oscillators",
author = "Smelyanskiy, {V. N.} and Luchinsky, {Dmitry G.} and Timucin, {D. A.} and A. Bandrivskyy",
year = "2005",
month = aug,
doi = "10.1103/PhysRevE.72.026202",
language = "English",
volume = "72",
pages = "026202",
journal = "Physical Review E",
issn = "1539-3755",
publisher = "American Physical Society",
number = "2",

}

RIS

TY - JOUR

T1 - Reconstruction of stochastic nonlinear dynamical models from trajectory measurements

AU - Smelyanskiy, V. N.

AU - Luchinsky, Dmitry G.

AU - Timucin, D. A.

AU - Bandrivskyy, A.

PY - 2005/8

Y1 - 2005/8

N2 - An algorithm is presented for reconstructing stochastic nonlinear dynamical models from noisy time-series data. The approach is analytical; consequently, the resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. The strengths of the algorithm are illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. The efficiency and accuracy of the algorithm are further demonstrated by inferring a model for a system of five globally and locally coupled noisy oscillators.

AB - An algorithm is presented for reconstructing stochastic nonlinear dynamical models from noisy time-series data. The approach is analytical; consequently, the resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. The strengths of the algorithm are illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. The efficiency and accuracy of the algorithm are further demonstrated by inferring a model for a system of five globally and locally coupled noisy oscillators.

KW - stochastic processes

KW - time series

KW - nonlinear dynamical systems

KW - noise

KW - oscillators

U2 - 10.1103/PhysRevE.72.026202

DO - 10.1103/PhysRevE.72.026202

M3 - Journal article

VL - 72

SP - 026202

JO - Physical Review E

JF - Physical Review E

SN - 1539-3755

IS - 2

ER -