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The Runge Kutta method is selected to solve this model.
The K-NN method is selected as the optimal (k) neighbours.
Therefore, the most appropriate method is selected with a primary concern for data availability.
In case the "mic" (maximum intersected candidates) method is selected, the error distance is increased.
For the experiments realized, the scaled conjugate gradient method is selected as the training algorithm.
For comparison, a second method is selected that is based on the S-transform [24].
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The regularization parameters of each method were selected by a model selection criterion AICc.
The standard addition method was selected as method calibration.
Bradford method was selected to determine the protein content.
The early stopping method was selected to overcome overfitting problem.
Taguchi method was selected in order to design experiments.
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