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The FSF model has been combined with a standard least-squares estimator to directly obtain an estimate of the process frequency response.
Therefore, a multidisciplinary approach is necessary, for which the comprehensive estimate of the process performances is not yet available.
The simple design assumes that the robust GMC reference system is parameterized in terms of a single variable, β, and requires an estimate of the process uncertainty.
We also reported an accurate estimate of the process parameters in order to maximize the Charpy impact energy in Al TiC laminated nanocomposite.
In the case that we have a conservative estimate of the process uncertainty, a data-driven optimizer is optionally applied to iteratively adjust the parameter β for an improved closed-loop performance.
The results also confirm that the proposed hybrid method offers an accurate estimate of the process change point, as compared to the most recent methods developed for the change point estimation.
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Statistical biogeographic methods such as DEC allow not only an examination of the biogeographic history of a clade and an estimate of the processes involved in producing that history (dispersal, vicariance and extinction), but also quantification of how confident we are in these reconstructions, via consideration of (relative) likelihoods.
Despite this apparent process/model mismatch acceptable estimates of the process states were obtained.
Next, a multifractal wavelet analysis is performed using the biomass estimates of the process.
The use of Kalman filtering to obtain optimal estimates of the process states in the presence of modeling inaccuracies, process disturbances and limited measurements is investigated.
Furthermore, the covariance matrix of the adjustments is used in order assist in the design of an improved network with respect to the number of flow-meters required so that reliable estimates of the process variables can be obtained.
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