## 5 Feature Fusion Based on Skewness Factor
### New Feature Verification  
After adding new feature 1 and new feature 2 based on the original features of the sample, in order to compare with the original neural network model, the other parameters of the neural network model are kept unchanged; only the size of the neural network is changed from 7 \(\times\) 30 \(\times\) 5 to 9 \(\times\) 30 *\(\times\) 5. Then training samples with different capacities are input into the neural network training in turn; the training process is shown in Figures 15-19. \begin{table}
\begin{tabular}{c c c} \hline \hline
**New Feature** & **Feature Fusion Method** & **Pearson Correlation Coefficient between Features** \\ \hline New feature 1 & Skewness \(\times\) Shape & \(-0.0498\) \\ New feature 2 & Skewness \(\times\) Crest & \(-0.0994\) \\ New feature 3 & Skewness \(\times\) Margin & \(-0.1462\) \\ New feature 4 & Skewness \(\times\) Impulse & \(-0.1470\) \\ New feature 5 & Skewness \(\times\) Kurtosis & \(-0.3538\) \\ New feature 6 & Skewness \(\times\) Absolute Mean & \(-0.3890\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: New features based on skewness factor fusion. \begin{table}
\begin{tabular}{c c} \hline \hline
**Number of New Features** & **Minimum Number of Iterations for 100\% Accuracy** \\ \hline
1 & 660 \\
2 & 550 \\
3 & Overfitting \\
4 & Overfitting \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Test result of new features with 50 samples. Figure 16: Seventy-five training samples of the neural network. Figure 17: One hundred training samples of the neural network. Figure 18: One hundred twenty-five training samples of the neural network. Figure 15: Fifty training samples of the neural network. After completing the training, four indexes for evaluating the training effect are counted, and the training effect of the model after adding the new feature is compared with the training effect of the previous model. The results are shown in Table 13. Similarly, the number of iterations for different sample size models to converge is also different, and the corresponding iterations are set according to the training results for the training under each sample size to test the training time. The results are shown in Table 14.  
After a comparison, we can draw the following conclusions:  
1. Compared with the previous model, the model with new features 1 (skewness \(\times\) shape) and new features 2 (skewness \(\times\) crest) has been obviously improved in the training effect, and the average accuracy of the classification has improved too. The standard deviation of classification accuracy, the least number of iterations, training time, and so on all improved, which shows that adding new features can improve the model's learning ability, learning stability, and the convergence rate of the calculation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Sample** & **Average Classification** & **Accuracy Standard** & **The Minimum Number** & **Training Time** \\
**Size** & **Accuracy** & **Deviation** & **of iterations** & **(s)** \\ \hline
50 & 0.9236 & 0.1168 & 880 & 12.61 \\
50 (new feature) & 0.9471 & 0.1102 & 611 & 12.54 \\
75 & 0.8556 & 0.1466 & Oscillation & 17.36 \\
75 (new feature) & 0.8783 & 0.0883 & Oscillation & 17.35 \\
100 & 0.9433 & 0.1026 & 630 & 22.95 \\
100 (new feature) & 0.9620 & 0.0916 & 488 & 23.18 \\
125 & 0.9566 & 0.0986 & 492 & 28.18 \\
125 (new feature) & 0.9730 & 0.0741 & 342 & 28.25 \\
150 & 0.9729 & 0.0744 & 365 & 33.41 \\
150 (new feature) & 0.9809 & 0.0666 & 293 & 33.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Different training sample size model training results indicators comparison. Figure 19: One hundred fifty training samples of the neural network.