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A joint state and parameter estimation scheme for nonlinear dynamical systems

Smith, P. J., Dance, S. L. and Nichols, N. K., (2014) A joint state and parameter estimation scheme for nonlinear dynamical systems. Technical Report. Dept of Mathematics & Statistics, University of Reading pp24.

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Abstract/Summary

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.

Item Type:Report (Technical Report)
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:50578
Uncontrolled Keywords:State estimation, parameter estimation, variational data assimilation, filtering, nonlinear dynamical systems.
Publisher:Dept of Mathematics & Statistics, University of Reading

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