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 1539-3755Full text not available from this repository.
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.
|Journal or Publication Title:||Physical Review E|
|Uncontrolled Keywords:||stochastic processes ; time series ; nonlinear dynamical systems ; noise ; oscillators|
|Subjects:||?? qc ??|
|Departments:||Faculty of Science and Technology > Physics|
|Deposited By:||Ms Margaret Calder|
|Deposited On:||09 Jun 2008 14:47|
|Last Modified:||29 Mar 2017 02:00|
Actions (login required)