A joint state and parameter estimation scheme for nonlinear dynamical systems
Data(s) |
10/12/2014
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Resumo |
We present a novel algorithm for concurrent model state and parameter estimation in nonlinear dynamical systems. The new scheme uses ideas from three dimensional variational data assimilation (3D-Var) and the extended Kalman filter (EKF) together with the technique of state augmentation to estimate uncertain model parameters alongside the model state variables in a sequential filtering system. The method is relatively simple to implement and computationally inexpensive to run for large systems with relatively few parameters. We demonstrate the efficacy of the method via a series of identical twin experiments with three simple dynamical system models. The scheme is able to recover the parameter values to a good level of accuracy, even when observational data are noisy. We expect this new technique to be easily transferable to much larger models. |
Formato |
text |
Identificador |
http://centaur.reading.ac.uk/50578/1/Smithetal_Preprint_MPS_14_26.pdf Smith, P. J. <http://centaur.reading.ac.uk/view/creators/90003622.html>, Dance, S. L. <http://centaur.reading.ac.uk/view/creators/90000823.html> and Nichols, N. K. <http://centaur.reading.ac.uk/view/creators/90000836.html>, (2014) A joint state and parameter estimation scheme for nonlinear dynamical systems. Technical Report. Dept of Mathematics & Statistics, University of Reading pp24. |
Idioma(s) |
en |
Publicador |
Dept of Mathematics & Statistics, University of Reading |
Relação |
http://centaur.reading.ac.uk/50578/ creatorInternal Smith, Polly J. creatorInternal Dance, Sarah L. creatorInternal Nichols, Nancy K. |
Tipo |
Report NonPeerReviewed |