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.
In this paper, BPANN was modeled for the prediction of DL, in which subjective merit of DL was set as output and linear sound pressure RMS amplitudes from five CBRs ranging from 20 to 500Hz were selected as inputs by correlation analysis between subjective DL and psychoacoustic metrics.
The control of DL was realized by ANE system by adjusting the gain coefficients of five sound pressure amplitude inputs from modeled BPANN. During the ANE process the accuracy of reference signal is a key point to the system's efficiency.
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.