## 4 Experiment and Discussion
### Experiment Results  
To verify the performance and effectiveness of the FNN with IPSO (abbreviated as IPSO-FNN), we randomly selected two subsets from the original dataset, and each subset was composed of 5000 samples. One was treated as the training set and another was treated as the testing set. After that, six datasets, named S1 to S6, were randomly selected from the training set to train the IPSO-FNN model, and the six training datasets contained 300, 400, 500, 600, 700, and 800 samples, respectively, as shown in Table 2. Among the six datasets, any two training datasets did not intersect. In the same way, six datasets were randomly selected from the testing set to test the IPSO-FNN model, and each testing dataset was composed of 100 samples. \begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Variable** \\ \hline \(x_{1}\) & Cutting motor current (CC) \\ \(x_{2}\) & Traction motor current (TC) \\ \(x_{3}\) & Cutting part temperature (CT) \\ \(x_{4}\) & Traction speed (TS) \\ \(y\) & The health grade of shearer \\ \hline \end{tabular}
\end{table} 
Table 1: The notations of variables. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset Number** & **Notation** & **Training Samples** & **Testing Samples** \\ \hline
1 & \(S_{1}\) & 300 & 100 \\
2 & \(S_{2}\) & 400 & 100 \\
3 & \(S_{3}\) & 500 & 100 \\
4 & \(S_{4}\) & 600 & 100 \\
5 & \(S_{5}\) & 700 & 100 \\
6 & \(S_{6}\) & 800 & 100 \\ \hline \end{tabular}
\end{table} 
Table 2: The selected six datasets. Figure 3: System noise analysis of cutting motor current: (**a**) the original signal of cutting motor current; and (**b**) the system noise information. In the experiment, IPSO-FNN was evaluated by comparing it with PSO-FNN (optimizing the parameters of FNN with basic PSO), GA-FNN (optimizing the parameters of FNN with a genetic algorithm), a single FNN, a BPNN (back-propagation neural network), and a SVR (support vector regression) on the evaluation of shearer health condition. The parameters of all optimization algorithms were selected as follow: population size: 50, maximal iteration: 200, the error precision: 0.0001. In IPSO-FNN and PSO-FNN models, \(\alpha_{\max}\) = 0.9, \(\alpha_{\min}\) = 0.4, \(c_{1}\) = 1.5, \(c_{2}\) = 2.5, [\(V_{\min}\), \(V_{\max}\)] = [-1, 1], \(H\) = 1. In GA-FNN model, the crossover probability and mutation probability were set to 0.8 and 0.05, respectively. For the single FNN and BPNN models, the number of nodes in the input layer was four, while there was only one output, so \(k\) = 4. The number of neurons in the hidden layer \(n\) was determined by \(\sqrt{k}+a\), where \(a\) is a constant between one and 15. Through repeated training tests, \(n\) was set to five in this paper. The learning rate was set to 0.05. For the SVR, the LIBSVM package developed by [PERSON] and [PERSON] in Taiwan University was used to construct the SVR model. In order to compare the advantages and disadvantages of the different methods quantitatively, five indices, including the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Theil's inequality coefficient (TIC), and training time (TT), were employed in this paper. The experiment results based on different methods are listed in Table 3. Additionally, the performance comparisons of the six models in MSE, MAE, MAPE, and TIC are illustrated in Figures 4-8. Figure 4: Comparison of mean square error. Figure 5: Comparison of mean absolute error. Figure 6: Comparison of mean absolute percentage error. As shown in Table 3 and Figures 4-7 we can see that the performance of IPSO-FNN, PSO-FNN, and GA-FNN is better than that of single FNN, BPNN, and SVR. The reason lies in the fact that the parameters of FNN, BPNN, and SVR are not suitable for the models to estimate the shearer health conditions, while the parameters of other FNNs are obtained by the corresponding optimization algorithms.