## 4 Experimental Results and Analysis
Different temperatures and pulling speeds produce different tail diameters and mass increments. In the second dataset, some basic processing parameters and control processing parameters are different. For the results in Figure 12, this paper uses the dataset with the same basic processing parameters for prediction. For the results of mass estimation, the predicted results deviate significantly from the actual results. The maximum error is tens of millimeters. One reason is that mass data are affected by changes in motor torque while reflecting changes in crystal mass. Another reason is that the accuracy of the weighing signal is also restricted by the accuracy and sensitivity of the weighing sensor itself. In Figure 12, we establish a prediction model with mass input and a prediction model without mass input. The same dataset is used for network training at the same time. Then, we use the trained network model to predict the crystal tail diameter. The effect diagram and error diagram between the two prediction results and the real value are shown in Figure 12. By comparing the results of two prediction models, the method DBN-ACO-SVR  
Figure 11: Iterative search results graph. Figure 10: The error results of the DBN iteration. considers the weighing signal, which can significantly reduce the prediction error, which is less than 10 mm. It can be used for the prediction and control of the tail process. For the results in Figure 13, the training set and test set used in this paper are not only different in control processing parameters, but also different in some basic processing parameters. Because the difference of basic processing parameters is not reflected in the prediction model, the prediction results become worse, which is still smaller than the error of directly using the mass signal to predict the tail diameter. The results show that the DBN-ACO-SVR prediction model with parameter identification has a low error and better prediction capability. To better visualize the superior performance of the soft measurement model based on DBN-ACO-SVR in predicting the crystal tail diameter, we compared a soft measurement model combined with DBN and SVR with a DBN-ACO-SVR synthetic model. The test set, according to the changing trend of crystal length, was selected for prediction. The final experimental results based on different models are shown in Figure 14.  
Figure 12: The results of different methods based on the same dataset. (**a**) Comparison of prediction results with or without mass. (**b**) Comparison of prediction results with mass. (**c**) Comparison of prediction errors. The root mean square error (RMSE) is a common index used to measure the difference between the predicted value and the actual observation value of the model. It calculates the mean value of the square of the difference between the predicted value and the actual value, and then takes its square root to obtain a measure of the overall error. A smaller RMSE value usually indicates that the model has better prediction accuracy, while a larger RMSE value indicates that the prediction performance of the model is poor [20]. DBN-ACO-SVR, DBN, and SVR were used to predict the tail diameters of crystals in different lots. The RMSE of the predicted results is shown in Table 1.  
The simulation results show that the accuracy of the DBN-ACO-SVR prediction model is better than that of other prediction models, and the model's prediction accuracy is higher. Figure 14: The results of different network models based on the same dataset. Figure 11.0 The error of the DBN-ACO-SVR prediction model with parameter identification based on the different dataset.