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Model uncertainty has two forms.
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The recognition that optimal control trajectories for batch processes can be highly sensitive to model uncertainties has motivated the development of methods for explicitly addressing robustness during batch processes.
However, the absence of DOB design guideline considering plant modelling uncertainty has hindered practical application of the DOB.
Also, model uncertainties have to be considered for evaluating scenario effects.
Even though the model uncertainties had significant effects on model results, they were in agreement with previous models results.
The model's uncertainty has been evaluated based on the comparison of the theoretical and experimental values of the bending resistance.
Over the years, data assimilation techniques, such as the Kalman filter, have become essential tools for improved system estimation by incorporating both models forecast and measurement; but its potential to mitigate the impacts of aforementioned model-form uncertainty has yet to be developed.
The different noise types used for (mathcal {H}_{0}) and (mathcal {H}_{1}) represent the model uncertainty that has to be taken into account when designing spectrum sensing algorithms.
Neither a state-space model nor its stochastic uncertainty has to be realized.
The model is useful because uncertainty has been shown to be present and central across disorders and to increase with comorbidity.
A numerical example is used to illustrate the model and the effect that price uncertainty has in the design of a mining project.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com