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The enzymatic reaction rate for the defined maltose and glucose concentration was estimated using the trained network.
Once the network was trained, the remaining 23 individuals were tested using the trained network.
All 496 data points from 35 data sets were used to train a general ANN model, then the solubilities were back-calculated using the trained network and MPD (±S.D).
Finally, by performing interpolation using the trained network, the void fraction values for some other conditions were predicted.
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With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor.
Input session key traces to neural network: The session key traces are used as inputs to the trained network.
Accuracy, specificity, and sensitivity can be used to test the performance of the trained network.
After the networks were trained, we used the trained networks to find the responsiveness condition corresponding to the testing set with the 10-fold cross-validation method.
We built a neural network model using the training database.
In other words, the MLP network is trained using a supervised learning algorithm which uses the training data to adjust the network weights and biases.
The networks were constructed and trained using the neural network toolbox of MATLAB [ 44].
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