The chief purposes of this study are to obtain precise values of the system parameters at the transition to instability with specified uncertainties and to determine how well the experimental results can be explained with existing theory.
It is built on an underlying physical process and associated model, a collection of experimental observations with specified uncertainties, algebraic surrogate models (response surfaces) representing parametric dependence of the physical-model predictions of the experimental observables on the uncertain parameters, and specialized constrained-optimization algorithms.
Although some scientists still prefer to claim that no data are available to specify uncertainties or find that novel ideas cannot be tested properly for lack of appropriate technology (cf. Collins 2010), others state that larger data sets (inclusive historic data sets) are meanwhile discoverable (Hunt et al. 2009), making ecological forecasting feasible.
Analyses as presented explicitly specify uncertainty of process knowledge and data used in future-oriented studies.
Thus there is no need to specify uncertainty and disturbances for this controller design in advance.
The control synthesis procedure is based on the H∞-optimization, which guarantees robust stability and performance within specified uncertainty bounds by taking into account the effects of unmodeled dynamics, sensor noise, and parametric errors.
Respiratory illness due to long-term NO2 intake requires a slightly different C-R functional form but still relies on a β with specified uncertainty.
Even if such a complex TP model is correctly specified, uncertainty in parameters can have a very high leverage on estimates of low-dose response, so what one gains from introducing more biological realism through the modeling will likely be lost in a sea of statistical uncertainty (Crump et al. 2010).
Within that model, the individual uncertainties further specify the uncertainty whether a specific influence contributes to explaining the data of the dependent variable.
The framework developed offers the ability to completely enclose the feasible solutions to the parameter identification problem, given specified measurement uncertainties for the natural frequencies.
