We investigate the accuracy of inference in a chaotic dynamical system (Duffing oscillator) with the Unscented Kalman Filter and quantify the dependence on the sample size and the signal to noise ratio. In order to improve convergence to the true parameters in the case of a bad initialization of the algorithm, we optimize the location of sigma points with Bayesian optimisation.
Inference with the Unscented Kalman Filter and optimization of sigma points
Michela Eugenia Pasetto;Umberto Noè;Alessandra Luati;
2017
Abstract
We investigate the accuracy of inference in a chaotic dynamical system (Duffing oscillator) with the Unscented Kalman Filter and quantify the dependence on the sample size and the signal to noise ratio. In order to improve convergence to the true parameters in the case of a bad initialization of the algorithm, we optimize the location of sigma points with Bayesian optimisation.File in questo prodotto:
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