Reconstruction of stochastic nonlinear dynamical models from trajectory measurements

Smelyanskiy, V. N. and Luchinsky, Dmitry G. and Timucin, D. A. and Bandrivskyy, A. (2005) Reconstruction of stochastic nonlinear dynamical models from trajectory measurements. Physical Review E, 72 (2). 026202. ISSN 1539-3755

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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.

Item Type:
Journal Article
Journal or Publication Title:
Physical Review E
Uncontrolled Keywords:
?? stochastic processestime seriesnonlinear dynamical systemsnoiseoscillatorsstatistical and nonlinear physicsstatistics and probabilitycondensed matter physicsqc physics ??
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Deposited On:
09 Jun 2008 13:47
Last Modified:
15 Jul 2024 11:40