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In the future, we will consider the efficiency of the a posteriori estimator to obtain an optimal estimate.
The corrected topology model is then fed into the state estimator and economic dispatch module to compute the optimal estimate of the system state and dispatch instruction, respectively.
There is still some room for improvement in the optimization of the device, and a more precise and optimal estimate of efficiency will be presented in our future work.
The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication.
The Kalman filter operates by producing a statistically optimal estimate of the system state based upon the measurements.
The Kalman filtering routines also calculate an optimal estimate of the gyro bias rate as a function of time.
This model is actively compared with real-time measurements to produce an optimal estimate for the mode's amplitude and phase.
This is achieved by deducing an online Bayes optimal estimate of the true measurand given uncertain, noisy or incomplete measurements within the framework of sequential Monte Carlo methods.
Some deterministic equivalents are found for the random rate of convergence of this optimal estimate, for deterministic and random designs with vanishing or diverging densities.
The first method focuses on the fault indicating signal to be an optimal estimate, in the H∞-norm sense, of the fault signal.
As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived.
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