# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## 5 Prediction Results and Analysis
### Analysis  
To better measure the prediction accuracy of the model, considering the influence of different sample sizes, the prediction results of multiple modeling with different sample numbers of the same model were integrated, and the comprehensive, accurate value \(C\), which reflects the prediction accuracy of different models under the same conditions, was used for comparison, as shown in Equation (7). \[C=\sum_{i=1}^{n}C_{i} \tag{7}\]  
where \(C_{i}\) is the number of accurate samples predicted by the model, \(i\)-th is the sample size and \(n\) is the number of types of sample size; in this paper, \(n=3\) (as 40,70 and 100). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Model** & **Index Dimensionless Method** & _A (40)/\%_ & _A (70)/\%_ & _A (100)/\%_ \\ \hline \multirow{4}{*}{Standard BP model} & Unified extreme value processing method & 70.0 & 60.0 & 75.0 \\  & Differential extreme value processing method & 77.5 & 48.6 & 59.0 \\  & Normalized processing method & 67.5 & 67.1 & 60.0 \\  & Data averaging processing method & 70.0 & 52.9 & 68.0 \\ \hline \multirow{4}{*}{GDM-BP model} & Unified extreme value processing method & 62.5 & 57.1 & 69.0 \\  & Differential extreme value processing method & 52.5 & 60.0 & 69.0 \\  & Normalized processing method & 67.5 & 54.3 & 62.0 \\  & Data averaging processing method & 67.5 & 57.1 & 66.0 \\ \hline \multirow{4}{*}{BFG–BP model} & Unified extreme value processing method & 72.5 & 70.0 & 89.0 \\  & Differential extreme value processing method & 65.0 & 70.0 & 81.0 \\  & Normalized processing method & 65.0 & 88.6 & 64.0 \\  & Data averaging processing method & 85.0 & 65.7 & 91.0 \\ \hline \multirow{4}{*}{SCG–BP model} & Unified extreme value processing method & 80.0 & 82.9 & 95.0 \\  & Differential extreme value processing method & 77.5 & 81.4 & 78.0 \\  & Normalized processing method & 60.0 & 70.0 & 45.0 \\  & Data averaging processing method & 65.0 & 70.0 & 91.0 \\ \hline \multirow{4}{*}{LM–BP model} & Unified extreme value processing method & 80.0 & 92.9 & 97.0 \\  & Differential extreme value processing method & 72.5 & 88.6 & 94.0 \\ \cline{1-1}  & Normalized processing method & 70.0 & 84.3 & 58.0 \\ \cline{1-1}  & Data averaging processing method & 67.5 & 92.9 & 95.0 \\ \hline \hline \end{tabular} Note: \(A\) (40), \(A\) (70) and \(A\) (100) refer to the prediction accuracy of the BP model when the sample size is 40, 70 and 100, respectively.  
\end{table} 
Table 2: Prediction results of the BP model. Figure 4: Model accuracy rate \(A\) under different dimensionless methods: (**a**) sample size 40; (**b**) sample size 70; (**c**) sample size 100. The larger the \(C\) value is, the higher the accuracy and the better the prediction effect. Table 3 shows the comprehensive, accurate values of different models. Table 3 shows that the dimensionless method has different influences on the predicted effect of the BP model, and the influence degree can be directly reflected by the extreme difference in the comprehensive accuracy value \(C\). The larger the extreme difference value is, the greater the difference in the prediction results of the model with the dimensionless method. Therefore, the extreme difference value of \(C\) can be used to measure the prediction stability of a BP neural network model. Because the smaller the range of the extreme difference value of \(C\) is, the closer the model prediction result is, different dimensionless methods have less influence on the BP neural network model. Prediction stability is the excellent performance of a model. The prediction stability of a model is good, which indicates that the data requirement of the model itself can be reduced. In other words, if the prediction stability of the model is good, it is unnecessary to use specific methods to process data. Of course, all of these are based on a small difference in forecast accuracy. Table 4 shows the extreme difference values of \(C\) for different dimensionless methods under the same model.