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Nor is the output process confined to simple prints.
The optimal architecture has been used to predict the output process parameter.
Then, system identification is applied to derive linear time-invariant reduced-order models, which relate the input process variables (agitator speed, solvent feed flowrate and concentration, feed concentration and flowrate) to the output process variables (raffinate concentration and extract concentration).
Under the assumption of independence among the random variables used in the Karhunen Loeve expansion and Independent Component Analysis representations, the latter provides more accurate statistical characterization of the output process for the specific cases examined.
In particular, this example has been studied rather extensively in connection to computation of entropy of the output process {Y n }, see e.g., [8 10, 12].
Take (mathcal{Y}(t)) for the von Neumann algebra generated by the output process (y(s)) for (0leq s leq t).
Similar(50)
Then, ([dy(t),dy^{T}(t)]=2J,dt), which means that the output processes are non-commutative.
Therefore, we have ([dy(t),dy^{T}(t)]=2J,dt), i.e., the output processes are non-commutative.
We have obtained non-commutative linear least mean squares estimators for linear QSDEs by extending Belavkin-Kalman filters to the case where the output processes are non-commutative.
SAM was carried out with 1000 permutations and the output processed to remove duplicates.
The output processes are marked by grey circles in Figure 3B.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com