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Two different techniques of Monte Carlo simulation are used to study the impact of parameter variability on target variables of nitrogen dynamics on the field scale.
target variables, is not measurable.
The target variables can be continuous or categorical.
As these virtual references correspond locally to the target variables, decreasing them reduces the target variables as well, until the limits of the corresponding Pareto set are reached.
The general regression neural network (GRNN) performs non-linear regressions when the target variables are continuous.
This set of vectors and associated target variables constitutes a training set.
Forest target variables can be difficult, time consuming and expensive to measure on the ground.
Two target variables should be taken into account in the evaluation of this last factor.
The center of each block is calculated in pair of the process and target variables respectively.
As target variables the grain size, density and mass loss were investigated.
Prediction models were obtained with a high accuracy in specific target variables.
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