## 5 Engineering Case Analysis
### PNN Classification and Identification  
Using the microseismic data obtained from field detection, 100 groups of coal-and-rock-mass-rupture signals and 100 groups of blasting-vibration signals were randomly selected. The energy entropy of the mine microseismic signal is calculated using the above theory, which is used as the feature vector. From the total sample, 160 groups of training samples and 40 groups of test samples are randomly selected. Assume that the number 1 represents the coal-and-rock-mass rupture signal, and the number 2 represents the blasting-vibration signal. The radial basis function (RBF) is used in the PNN, and its weight and threshold are determined by the learning of training samples without iteration. In order to verify the effectiveness of the method in this paper, the generalized regression neural network (GRNN), feedforward neural network (BPNN) and PNN are used for comparative experiments. Among them, the hidden-layer node \(l=\log_{2}{n}\) of the network structure is selected by the BPNN, where \(l\) is the number of hidden-layer nodes, and \(n\) is the number of input-layer nodes. The transfer function used in the hidden layer is tan-sigmoid, and the training error and times are set to 0.0001 and 1000, respectively. In the generalized-regression-neural-network (GRNN) model, the accuracy of the data-prediction results depends on the setting of the smoothing factor. The better the set value, the more \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Signal Type** & **FM1** & **FM2** & **FM3** & **FM4** & **FM5** & **FM6** \\ \hline Rupture signal & 0.667 & 0.684 & 0.305 & 0.137 & 0.002 & 0.001 \\ Blasting signal & 0.689 & 0.761 & 0.151 & 0.062 & 0.011 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlation coefficient of different PF components. Figure 8: Energy-entropy-comparison diagram of typical microseismic signals in mines. accurate the data-prediction results. In this paper, the cross-validation method is used to select the smoothing factor, and the training-sample data are randomly divided into 10 sets, 9 of which are used for the training data set, and the remaining 1 data set is used to verify the data set, check the test error corresponding to each value, and search for the output value. The smoothing factor corresponds to the minimum mean square error of the actual values. The four feature vectors are freely combined, and finally 10 types of input vectors are obtained, which illustrate the efficiency of the method in this paper. Model 1 takes FM1 as the input vector. Model 2 takes FM1-FM2 as the input vector. Model 3 takes FM1-FM3 as input vectors. Model 4 takes FM1-FM4 as input vectors. Model 5 takes FM2 as the input vector. Model 6 takes FM2-FM3 as the input vector. Model 7 takes FM2-FM4 as the input vector. Model 8 takes FM3 as the input vector. Model 9 takes FM3-FM4 as the input vector. Model 10 takes FM4 as the input vector. The GRNN and BPNN use the same test set and sample set as the PNN. Figure 9 shows the classification accuracy of the training samples of different models. It can be seen from the figure that the training samples of different models achieved an accuracy rate of more than 90%, and the three methods obtained good learning effects. However, the accuracy difference is more obvious, especially when there is only one feature vector (models 1, 5, 8 and 10), and the training-sample accuracy is the lowest. The fourth group of models has the highest training accuracy, with the BPNN and GRNN all reaching 97%, and the PNN reaching 98%. The method in this paper shows better learning efficiency. The prediction results of different input models and different classifiers are shown in Figure 10. The PNN classifier proposed in this paper shows better prediction results under different input models compared with the prediction results of the GRNN and BPNN classifiers. Especially when FM1-FM4 are used as input variables, the prediction result of the FNN classifier is the best.