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The necessary computation time for both the models' training and evaluation in each test is measured and compared.
For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted.
It consists of five main steps: outliers detection, trend analysis, data normalization, probabilistic forecasting models training, and load variation and temperature uncertainty combination.
To build these models, training and testing of the network by using experimental results from 144 specimens produced with 16 different mixture proportions were conducted.
In the second scenario with BA, we can observe that the SVR model fitness and the ngspice fitness varies, this is because of the models training and the scale of the Y-axis, which is much smaller for this example.
For purpose of building these models, training and testing using the available experimental results for 195 specimens produced with 33 different mixture proportions were gathered from the technical literature.
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The ANN models trained and validated by representative data generally outperform those by using random data.
To build the model, training and testing using experimental results from 120 specimens were conducted.
To construct the model, training and testing was conducted by using experimental results from 82 specimens.
Quiescent conditions were simulated using 10 years for model training and 1 year for model validation.
The remaining 3993 observations were used for model training and ensemble selection.
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