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Compared with the existing results, the system states are unmeasurable and the bounds of model uncertainties and the disturbances only exist but are unknown.
The following assumptions are considered: (i) the biomass and chemical oxygen demand concentrations are unknown, (ii) the model parameters are unknown and vary but they are bounded, and (iii) the upper or lower bounds of model parameters are unknown.
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Recent research has established the utility of adaptive bounds on model uncertainty in adaptive approximation-based control.
The method of constructing uncertainty bounds on model predictions is described in detail in the Supplemental Material (Characterizing Uncertainty, pp. 28 29).
With regard to the engineering application, the successful identification of the true model by minimizing a multimodal misfit functional for wide feasible bounds of the model parameters confirms that waveform inversion by the improved global optimization method is promising for practical applications with real measurement data.
We compare our model to real-life security APIs, and argue that designing new systems within the bounds of the model improves safety, eliminating many common security issues.
Also, the approximate confidence bounds of the model parameters are obtained.
The bounds of the model uncertainties and external disturbances are assumed to be unknown in advance.
The bounds of both model uncertainties and external disturbances are assumed to be unknown in advance.
We then investigate path properties and moment bounds of the model.
Consequently, we aim to determine upper and lower bounds of the model response for a fixed set of parameters, which we label confidence bounds.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com