## 5 Engineering Case Analysis
### PNN Classification and Identification
Therefore, this paper specifically selects Model 4 with FM1-FM4 as input variables for analysis, and obtains the optimal prediction results calculated by different classifiers, as shown in Table 2. Figure 10 shows the comparison results of the calculation time of different models and different classifiers. Figure 9: Accuracy comparison of training samples of different input models and different classifiers. The classification prediction and recognition results of microseismic signals are shown in Table 2. It can be seen from the classification and prediction results of the three network models that the microseismic-event-classification algorithm based on the PNN has high accuracy, with an average accuracy rate of 90%, which is significantly higher than the accuracy rates of the BPNN at 75% and 82.5%. It can be more effective and accurate in identifying the category of microseismic events. The recognition effect for the blasting-vibration signal is the best. The recognition accuracy of the BPNN is 80.0% for the randomly selected 20 sets of prediction data, and the accuracy rate of the GRNN is improved to 85.0%. The best method in this paper is 95.0%; the correct recognition effect of coal-and-rock-mass-rupture signals is slightly worse than that of blasting-vibration signals. The accuracy rates of the BPNN, GRNN and the method in this paper are 70.0%, 80.0% and 85.0%, respectively. The generalization performance is greatly improved, with obvious recognition advantages. Overall, the average accuracy of the blasting signal of the three methods is 86.7%, which is higher than the 82.5% of the blasting signal of the coal-and-rock mass, and there is still a lot of room for improvement. In order to evaluate the quality of the PNN, this paper adopts the receiver-operating-characteristic (ROC) indicator, the area-under-curve (AUC) value, and precision. ROC takes the negative-positive rate (false-positive rate, FPR, specificity) as the horizontal axis and the true rate (true-positive rate, TPR, sensitivity) is the curve of the vertical axis, which reflects the sensitivity to the same signal stimulus. The more the curve deviates from the 45-degree diagonal, the more accurate the model classification is. The ROC curves of the three classification models that select the optimal eigenvector model are shown in Figure 11. In Figure 11, we have plotted the average ROC with different colors and bold lines to show the overall performance of the mode (the gray dashed line reflects the ROC of random guessing). From the degree of deviation, it can be seen that the classification accuracy of the PNN is better than the RGNN, and the RGNN is better than the BPNN. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{2}{c}{**Blasting Signal (20 Groups)**} & \multicolumn{2}{c}{**Rupture Signal (20 Groups)**} & \multicolumn{2}{c}{**Total Result (40 Groups)**} \\ \cline{2-7}  & **Exact Number** & **Accuracy** & **Exact Number** & **Accuracy** & **Exact Number** & **Accuracy** \\ \hline BPNN & 16 & 80.0\% & 14 & 70.0\% & 30 & 75\% \\ GRNN & 17 & 85.0\% & 16 & 80.0\% & 33 & 82.5\% \\ PNN & 19 & 95.0\% & 17 & 85.0\% & 36 & 90\% \\ Total & 52 & 86.7\% & 47 & 78.3\% & 99 & 82.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of predicted optimal results of different classifiers. Figure 10: Accuracy comparison of different input models and different classifiers. The value of AUC is the area under the ROC curve. Obviously, the value of this area cannot be greater than 1, and the value range is [0.5, 1]. Combined with the ROC curve, the classifier with the larger AUC has the better effect. This value can better reflect the actual classification sensitivity of data. Precision is an evaluation index for prediction results. It represents the percentage of the results predicted by the model as positive samples that are actually positive samples. Figure 12 lists the comparison results of the AUC value and precision of the three methods in this paper.