## 5 Experiments
### Construction of Shearer Drum Height Prediction Model Based on AR-AIGA-FRRBFNN
#### 5.2.3 Prediction Results of Shearer Drum Height Based on AR-FRBF  
It can be seen from Tables 12 and 13 that the AR-AIGA-FRBF model can better extract the nonlinear variation features of the learning cut height, and obtain more accurate predictions. Through rough set significance attribute reduction, the prediction ability of the network can be effectively improved. Relative to the AIGA-FRBF model, AR-GRU model, AR-SRU model, AR-RBF model, MSLSTM model and AFRPN model, the values of the shearer left drum height test set predicted by the AR-AIGA-FRBF model were as follows: MAE decreased by 43.0%, 67.3%, 63.2%, 71.2%, 55.6%, and 50.4%, respectively; MAPE decreased by 43.5%, 67.2%, 63.4%, 71.3%, 54.9%, and 52.2%, respectively; and RMSE decreased by 41.8%, 68.1%, 64.4%, 70.7%, 55.1%, and 51.0%, respectively. Similarly, relative to the AIGA-FRBF model, AR-GRU model, AR-SVR model, AR-RBF model, MSLSTM model and AFRPN model, the values of the shearer right drum height test set predicted by the AR-AIGA-FRBF model were as follows: MAE decreased by 29.5%, 61.9%,  
Figure 6: Comparison of height prediction of left drum of shearer with different model. (**a**) Prediction trajectory and error of shearer left drum based on AR-AIGA-FRBF and AIGA-FRBF models; (**b**) Prediction trajectory and error of Shearer left drum based on AR-SVR model; (**c**) Prediction trajectory and error of shearer left drum based on AR-GRU model; (**d**) Prediction trajectory and error of shearer left drum based on AR-RBF model; (**e**) Prediction trajectory and error of shearer left drum based on MSLSTM model; (**f**) Prediction trajectory and error of shearer left drum Based on AFRPN model. Figure 7: Comparison of height prediction of right drum of shearer with different model. (**a**) Prediction trajectory and error of shearer right drum based on AR-AIGA-FRBBF and AIGA-FRBBF models; (**b**) Prediction trajectory and error of Shearer right drum based on AR-SVR model; (**c**) Prediction trajectory and error of shearer right drum based on AR-GRU model; (**d**) Prediction trajectory and error of shearer right drum based on AR-RBF model; (**e**) Prediction trajectory and error of shearer right drum based on MSLSTM model; (**f**) Prediction trajectory and error of shearer right drum Based on AFRPN model. For the AR-SVR model, the poor performance may be due to the large dataset and large data dimensions, both of which would affect the prediction of the shearer drum height. For the AR-GRU and AR-RBF neural network models, the reason for the large prediction error may be that the fuzzy decision of shearer drum height affects the training of neurons. For the MSLSTM and AFRPN models, the prediction accuracy is not as high as the prediction model proposed in this study. The reason may be the redundancy of characteristic attributes in the parameter list. To reduce the influence of prediction error on the actual application process at a fully mechanized mining face, some mechanisms should be added. A soft sensor is a model for industrial process monitoring, control, and optimization. It can evaluate some parameters that are difficult to measure, and integrate with other models to improve the performance of the model [39; 40]. The soft sensor model can optimize the prediction performance to a certain extent. In addition, we can adjust the maximum number of training samples of AR-AIGA-FRRBFNN, or optimize the calculation method of discrete input data to further reduce the prediction error and adapt it to the needs of working conditions. This will be the focus of our future research. It should be noted that the reliability of model is based on multiple data samples and multiple data dimensions. When the data sample is small or the data dimension is insufficient, it may be difficult to obtain enough decision rules to support the model. Therefore, the application problem of less data samples or data dimensions is not within the scope of application of the model.