## 5 Experiments
### Construction of Shearer Drum Height Prediction Model Based on AR-AIGA-FRRBFNN
#### 5.2.2 AR-AIGA-FRRBFNN Model and Evaluation Method  
According to the shearer drum height decision rules in Tables 7 and 8, the network structure parameters of the shearer left and right drum height prediction model can be determined. The number of input layer neurons of the left drum height prediction network model is 8; the number of neurons in the hidden layer is 864, which represents the number of decision rules; the number of neurons in the fuzzy rough membership layer is 3, which represents the rule set with the same decision value; and the number of neurons in the output layer is 1, which represents the predicted shearer drum height. Similarly, the number of neurons in the input layer of the right drum height prediction network model is 8, the number of neurons in the hidden layer is 778, the number of neurons in the fuzzy rough membership layer is 3, and the number of neurons in the output layer is 1, representing the shearer drum height predicted by the network. The parameters of the adaptive immune genetic algorithm include the population size \(Q\), the initial crossover rate \(C_{1}\) and \(C_{2}\), and the initial mutation rate \(V_{1}\) and \(V_{2}\). In this study, AIGA operating parameters were determined by repeated experimental tests. We determined the population size to be \(Q=100\), the maximum number of iterations to be \(T=50\), the initial crossover rates to be \(C_{1}=0.65\), \(C_{2}=0.25\), the initial mutation rates to be \(V_{1}=0.03\), \(V_{2}=0.02\), the original number of iterations to be \(g=0\) and \(e=0.01\), and the fitness objective function to be \(F_{goal}=0.9901\). The number of training sample sets was 90% of \(l\) and \(r\), and the size of datasets were 6241 and 6059, respectively. The remaining 10% was used as test sets, and the size of datasets were 694 and 674, respectively. In order to verify the reliability of the control model proposed in this study, the attribute reduction sets \(p_{l}\) and \(p_{r}\) were respectively input into the radial basis function neural network (RBF), support vector regression (SVR), gated recurrent unit neural network (GRU) three regression prediction models for training and prediction, and the attribute reduction set and the dataset without attribute reduction were input into the AIGA-FRRBFNN model for training and prediction comparison. Finally, these were compared with the roller height adjustment model, with the Memory Strengthen Long Short-term Memory (MSLSTM) [12], and with the adaptive fuzzy reasoning Petri net (AFRPN) [16] model proposed in the recent study. Through repeated experiments, this study determined the best super parameters of the above prediction model and improved the prediction accuracy of the comparison model, as shown in Table 9.  
In Table 9, the LIBSVM toolkit is used for SVR experiments. By calling newrbe function in matlab, the RBF neural network is established for the experiment. In order to evaluate the regression prediction effect of the model, this study compared six models, namely FRRBF, RBF, SVR, GRU, MSLSTM [12] and AFRPN [16], based on the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Right Drum Height Decision Rule**} \\ \hline
1 & \(c_{1}(5)\) AND \(c_{2}(2)\) AND \(c_{4}(2)\) AND \(c_{6}(2)\) AND \(c_{8}(1)\) AND \(c_{10}(7)\) AND \(c_{11}(3)\) AND \(c_{15}(2)=d_{r}(0)\) \\
2 & \(c_{1}(2)\) AND \(c_{2}(4)\) AND \(c_{  
In this study, we used MATLAB R2018b for program development and performed our experiments on a hardware platform with an Intel(R) Core(TM) i5-6500 CPU @ 3.20 GHz, 16.0 GB RAM, and the Windows 7 64-bit operating system. Based on AR-AIGA-FRBF, AIGA-FRBF, AR-GRU, AR-SVR, AR-RBF, MSLSTM, and AFRPN, the height prediction of the training samples of the left and right drums of the shearer is shown in Tables 10 and 11.