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Estimation of parameters and unobserved components for nonlinear systems from noisy time series

  • We study the problem of simultaneous estimation of parameters and unobserved states from noisy data of nonlinear time-continuous systems, including the case of additive stochastic forcing. We propose a solution by adapting the recently developed statistical method of unscented Kalman filtering to this problem. Due to its recursive and derivative-free structure, this method minimizes the cost function in a computationally efficient and robust way. It is found that parameters as well as unobserved components can be estimated with high accuracy, including confidence bands, from heavily noise-corrupted data.

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Metadaten
Author:Andre Sitz, Udo Schwarz, Jürgen KurthsORCiDGND, Henning U. Voss
URL:http://pre.aps.org/
Document Type:Article
Language:English
Year of first Publication:2002
Year of Completion:2002
Release Date:2017/03/24
Source:Physical Review / E. - 66 (2002), S. 016210
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
Institution name at the time of publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik