Abstract
This letter proposes a novel Gaussian mixture model-based ensemble Kalman filter approach to the accurate calibration of the parameters of machine dynamic models. This approach aims to overcome some practical challenges affecting parameter calibration accuracy. Results show the proposed approach can provide precise calibrated parameters even when the machine operates under unbalanced network conditions with nonGaussian measurement noises.
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