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A novel adaptive observer is proposed to perform on-line estimation of both state and parameters of the electrical part of induction motors.
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The best data assimilation scheme for hydrologic modeling involved using a combination of streamflow, soil moisture, and snow water equivalent while performing both state and parameter updating.
Open image in new window Figure 6 Flow chart of the assessment procedure for both state and parameter.
For the DA-based approach (i.e., Eq. (30)), only state samples are accounted for, while for the IS-based approach as in Eq. (41), both state and parameter samples are taken into account.
We used kernel smoothing to reduce the effect of particle depletion, which is common when estimating both states and parameters with SISR.
Estimation of initial values of both states and parameters should be incorporated into the EKF algorithms in the future.
In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models.
Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task.
Unlike the maximum likelihood estimate or EM algorithms where the initial values of the states and their covariance matrix can be optimally estimated, a quite open subject for the dual EKF is the choice of the initial values for both states and parameters.
Apart from identifying suitable lumps in a novel and automatic way, the method also finds analytical expressions for the back-translation relations regarding both states and parameters between the original and the reduced models, resulting in the derivation of a zoomable model.
In this paper, both state variables and parameters of one-dimensional open channel model are estimated using a framework of the Ensemble Kalman Filter (EnKF).
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