BPANNBack Propagation Artificial Neural Network
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By several trial and error runs, it is found that the BPANN models with Nr > 300 require long training times and have poor classification performances.
The BPANN and DFP-LSSVM attain the best overall CAR when the Laplacian pyramid's level = 4.
For BPANN, various numbers of hidden layers have been trained to find the best classification rate.
Overall, nonlinear SVM outperforms BPANN, as the BPANN suffers from the existence of multiple local minimal solutions.
The ROC of the two methods is shown in the Figure 2; from the figure, the AUROC of the BPANN is larger than PCLR.
In addition, the training phases of BPANN and CT require a proper setting of their model hyper-parameters.
If there are M input-output pairs in the training data in total and the number of the neurons in the output layer is K, then the objective function of the BPANN is given by
In this study, a BPANN is trained to predict the shrinkage and warpage of injection-molded thin-wall parts.
Back propagation artificial neural network (BPANN) which was presented by Remelhart in 1986 is one of the most widely used neural network models at present.
Li et al., "Combining BPANN and wavelet analysis to simulate hydro-climatic process--a case study of the Kaidu River, NW China," Frontiers of Earth Science, vol.