## 3 Results and Analysis
### Path-Tracking Deviation Pattern Identification Test
#### 3.1.2 Performance Testing of Models
Differences between models: Combining the analysis of figures and tables and comparing the results of Models I, II, and III in the two tests, although the performance of all three models is excellent, the stability and prediction accuracy of Model III are relatively poor. Difference analysis: There are some differences in the performance of the three models; as analyzed by Sections 2.2.1 and 2.2.2, the linear correlation between the data features and labels of models I and II is higher, and the difference characteristics between different labeled data are significant. The feature structure of Model III is relatively fuzzier, the feature deviation between different labeled data is smaller, and the weight update rate for hidden features is lower, so the final generated model has poorer discrimination performance. Comparative validation tests of different deep learning methods  
In order to verify the validity and superiority of the present method, this study refers to the traditional classification prediction methods and constructs Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) [28] for predicting the data under each deviation pattern and compares them with this study. Among them, through careful tuning of the hyperparameters, we determined the optimal performance parameters for each model: the structure of RNN is configured as 3 layers of 128 nodes, with a learning rate of 0.05 and a batch size set to 10; the structure of LSTM is configured as 2 layers of 128 nodes, with a learning rate of 0.05 and a batch size set to 30. The two models incorporate a learning-rate decaying strategy. Models I, II, and III were trained for each of DBN, RNN, LSTM, and MAE, and Macro-F1 and Accuracy were used as performance metrics. Table 14 shows the data of the combined prediction performance metrics of various deep learning algorithms for each deviation pattern. By data analysis, the overall performance metrics of the models constructed using the DBN approach are better than RNN and LSTM, with better discrimination performance, model variability, and accuracy in the three deviant morphology scenarios. However, DBN, RNN, and LSTM all show variability between Models I, II, and III trained in different deviation morphology scenarios. The performance of Model III of DBN and RNN is more obvious compared to Models I and II, which is in line with the pattern described in the previous section. The performance of Models I and III of LSTM is basically the same, and the LSTM is more sensitive to the transient features' extraction ability than RNN. An ablation study of different deep learning methods  
Further, in order to compare the robustness of various types of deep learning methods, ablation comparison tests are carried out for DBN, RNN, and LSTM in the scenario of Model I. Since the internal structure of each method is different, a proportional reduction method is used for the ablation level: for DBN, RNN, and LSTM, the number of nodes in each layer is reduced in the proportion of 0%, 25%, and 50% without changing other hyperparameters. In order to comprehensively evaluate the performance of the three deep learning methods, Macro-F1 is used as the evaluation index, and finally, the variation in the recognition performance of each method is shown in Figure 13.  
The comparative result of Figure 13 is analyzed to show that the Macro-F1 values of DBN, RNN, and LSTM show inconsistent differences at different ablation levels. The colors in the figure are from dark to light, indicating that the Macro-F1 values are from high to low. As can be seen from the figure, the Macro-F1 value of DBN decreases from 0.9863 to 0.9123 under different ablation levels but shows the most stable and excellent performance among the three deep learning methods.