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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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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.

Item Type: Article
Journal or Publication Title: Physical Review E
Uncontrolled Keywords: stochastic processes ; time series ; nonlinear dynamical systems ; noise ; oscillators
Subjects: Q Science > QC Physics
Departments: Faculty of Science and Technology > Physics
ID Code: 9427
Deposited By: Ms Margaret Calder
Deposited On: 09 Jun 2008 14:47
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 18:36
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/9427

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