# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 4 Experiments
### Forecasting Model Test  
The prediction ability of the developed model is measured by comparing the data that do not belong to the model training set with the data predicted by the model. The overload and speed values of the first 24 sampling points are input into the model combing with the attack angle, bullet weight, bullet diameter, bullet length, etc., and the output is used as the input of the next time point to predict the subsequent overload curve. The comparison between the predicted overload curve for multilayer and thick targets and the real overload curve produced for the test set is shown in Figures 12 and 13.  
Figure 12: Prediction and actual overload curves for a multilayer target. (**a**) Global map of the multilayer target prediction results; (**b**) partial enlarged view of the multilayer target prediction results; (**c**) global map of the multilayer target prediction results; (**d**) partial enlarged view of the multilayer target prediction results.  
Figure 12a,b are predictions using simulation data, and Figure 12c,d are predictions using live ammunition data. It can be seen from the global and local magnified prediction graphs in Figure 12a,b that the overload prediction for multilayer targets is highly consistent, and there is a slight deviation in the amplitude prediction for the peak overload value, which can meet the demand of the overload curve prediction. It can be seen from Figure 12c,d that although the prediction based on live ammunition data has some deviation in amplitude, the basic process of target penetration can be consistent. Figure 13a,b are predictions using simulation data, and Figure 13c,d are predictions using live ammunition data. It can be seen from Figure 13a,b that the prediction effect based on simulation data is basically consistent, with only a slight deviation in amplitude. It can be seen from Figure 13c,d, the global prediction map and the local zoomed in region, respectively, that the overload prediction trend for thick targets is basically consistent, but the prediction gap between specific amplitudes is obvious. The large gap between multilayer and thick targets is mainly due to the periodic changes in the multilayer target data, the obvious changes in the multilayer target overload, and the fact that the model's attention mechanism easily focuses on the locations with obvious changes. In contrast, the thick target data span is long, the change trend is mild, and the model attention mechanism has difficulty focusing, leading to inaccurate prediction results. At the same time, the gap between the prediction based on simulation data and the prediction based on live ammunition data is that the training set is dominated by simulation data, so the prediction effect for simulation data is better.  
Figure 13: Prediction and actual overload curve for a thick target. (**a**) Global map of thick the target prediction results; (**b**) partial enlarged view of the thick target prediction results; (**c**) global map of thick the target prediction results; (**d**) partial enlarged view of the thick target prediction results.