Reconstruction of stochastic nonlinear dynamical models from trajectory measurements

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 dyn...

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Bibliographic Details
Published in:Physical review. E, Statistical, nonlinear, and soft matter physics Statistical, nonlinear, and soft matter physics, 2005-08, Vol.72 (2 Pt 2), p.026202-026202, Article 026202
Main Authors: Smelyanskiy, V N, Luchinsky, D G, Timuçin, D A, Bandrivskyy, A
Format: Article
Language:English
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Summary: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.
ISSN:1539-3755
1550-2376
DOI:10.1103/PhysRevE.72.026202