## 3 Improved Grey Wolf Optimizer Algorithm to Optimize Support Vector Machine
### Improved GWO Algorithm
Meanwhile, the paper applied the ICEEMDAN method to process the cutting sound signal. It used the correlation coefficient method to select the characteristic IMF component and calculated its CMPE as the characteristic value. Furthermore, this method took the CMPE of the signal as the input of the cutting pattern recognition model based on IGWO-SVM after normalization, and took the shearer cutting pattern as the output. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**IMF Components** & \(IMF_{1}\) & \(IMF_{2}\) & \(IMF_{3}\) & \(IMF_{4}\) & \(IMF_{5}\) & \(IMF_{6}\) & \(IMF_{7}\) & \(IMF_{8}\) & \(IMF_{9}\) & \(IMF_{10}\) & \(IMF_{11}\) \\ \hline correlation coefficient & 0.3504 & 0.2988 & 0.263 & 0.3474 & 0.4616 & 0.3419 & 0.1446 & 0.2758 & 0.1233 & 0.2688 & 0.0804 \\ \hline \end{tabular}
\end{table} 
Table 2: The correlation coefficient of the IMF component. Figure 7: The mean value and standard deviation curve of composite multi-scale permutation entropy. The parameters of the IGWO-SVM cutting pattern recognition model were set as follows. The grey wolf population size was 20. The maximum number of iterations was 100. Meanwhile, the lower and upper bounds of the scaling factor were 0.2 and 0.8. The crossover probability was set to 0.2. Moreover, the value ranges of the SVM model parameters c and g were both [0.01, 100]. The mean square error was selected as the fitness function of the IGWO-SVM model. When the mean square error value reached the minimum, the target parameter value was close to the optimal value. The optimization curve of the IGWO-SVM model is shown in Figure 8.

Figure 8 indicates that the IGWO-SVM model tended to converge around the fifth generation. The fitness fluctuated in a small range in the optimization process to avoid the model falling into the local optimum. It could thus be determined that the best parameter combination of \(c\) and \(g\) was [2.92, 92.06]. Using the optimized parameter IGWO-SVM model for cutting pattern recognition, the recognition accuracy reached 97.78%. The cutting pattern recognition result is shown in Figure 9.

In order to verify the performance of the proposed IGWO-SVM model, this study applied ABC, PSO, GWO, and IGWO to optimize the SVM. The initial population size of the four models was set to 20 and the maximum number of iterations was set to 100. The number of honey sources in the ABC algorithm was 10. Meanwhile, the maximum number of searches was 20. Furthermore, the learning factors of the PSO algorithm were 1.5 and 1.6, respectively, and the inertia weights were 0.4 and 0.9. The identification and comparison results of the four models obtained by the ten-fold cross-validation method are listed in Table 3. The accuracy error was the highest accuracy minus the lowest accuracy. Similarly, the time-consuming error was the maximum recognition time \(-\) the minimum recognition time. Figure 8: Fitness optimization curve. Figure 9: Cutting pattern recognition results. Table 3 shows that using the CMPE as the feature value, the cutting pattern recognition rate of the four models was relatively high. The average recognition rate of the PSO-SVM model was 95.61%, which was the lowest. Meanwhile, the accuracy error was the largest, reaching 3.84%. Furthermore, the average recognition time was 0.74s, which was the highest. The PSO-SVM model had the most unstable performance. The average accuracy of the ABC-SVM and GWO-SVM models was 96.11%. However, the accuracy error of the ABC-SVM model was 3.34%, while that of the GWO-SVM model was 0. The average accuracy of the IGWO-SVM model was the highest, reaching 97.67. Its accuracy error was 0.56%. Meanwhile, the average recognition time and time-consuming error of the IGWO-SVM model were the lowest. The above results show that the IGWO-SVM recognition model proposed in this paper demonstrated excellent performance for shearer cutting pattern recognition. ## 6 Conclusions

This paper proposed a method of sound signal recognition for shearer cutting based on the ICEEMDAN and IGWO-SVM models.