BPANNBack Propagation Artificial Neural Network
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In the past, the prediction performance of a trained BPANN was usually conducted by choosing randomly and manually several testing pairs from the experimental data that was never seen by the BPANN.
Comparison of Prediction Performance of BPANN and Taguchi's Method in Determining Quality Factors (Shrinkage and Warpage) and Optimal Process Condition
This study also uses the optimal process condition suggested by Taguchi's method as the input layer of the trained BPANN, and compares the shrinkage and warpage predicted by its output layer with the predicted value of Taguchi's method.
In order to compare the optimal process conditions obtained by the Taguchi's method and BPANN, this study uses the trained BPANN and Taguchi's method to determine the optimal process conditions.
Before discussing the BPANN training quality and prediction performance, this study investigates the effect of initial values of weight and bias, which are randomly produced by the computer, on the training quality of the BPANN under base-line condition A5.
It is noted that the predicted values by the BPANN are far better than the C-Mold simulation value in terms of agreement with experimental data.
Thus, it is noted that the selection of testing pairs from experimental data has an extremely large influence on the estimation of the prediction performance of a BPANN.
To compare the prediction performance of the BPANN and Taguchi's method in determining quality factors (observed factors), this study calculates the quality factors (shrinkage and warpage) under the optimal process condition determined by Taguchi's method (6), using both the trained BPANN (base-line condition A5) and Taguchi's method.
Table 6 lists the optimal process conditions determined by the BPANN under different number of divisions N.
Effect of Structural Parameters of BPANN on Training Quality