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Select the input variables.
The correlation coefficient between the input variables is calculated.
The first step is to fuzzify the input variables.
The input variables are average CPU load and memory usage.
Therefore, all of the input variables used are non-dimensionalized.
The first layer supplies the input variables to the next layer and formed from the input variables' membership functions (MFs).
Preprocessing of the input variables may change the situation.
Predicted RTD from the input variables had a r2 > 0.96.
The input variables for member models were selected by a hybrid filter and wrapper method.
The input layer has the same number of neurons as the input variables of the model.
Otherwise, user can modify the input variables and restart the pipeline.
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