# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 4 Experimental Simulation Results and Analysis
### Results and Analysis  
First, we need to determine the number of neurons. Figure 10 shows the influence of the number of neurons in the hidden layer on the model performance of the extended ELM model. This figure shows the process of analyzing and selecting the number of hidden layer neural units in this experiment, and finding the appropriate value by debugging different numbers of neural units. From left to right, the abscissa indicates the number of concealed layers from 100 to 2000. The value of accuracy, precision, and recall is represented by the ordinate. The graphic shows that, as the number of hidden layers increases, the accuracy, precision, and recall rise, and the model performance improves. When the number of hidden layers is 1500 and more, the performance of the model tends to be stable. To achieve the optimal effect of the model, the final extended ELM model uses 1600 hidden neural units.  
The extended ELM model has shown excellent results. Figure 11 shows the confusion matrix of each category. The abscissa represents the prediction category, the ordinate represents the real category, and the sample on the diagonal is the correct prediction sample. The number in the matrix represents the corresponding number of samples. The lower the number of samples, the darker the corresponding color in the matrix and the closer it is to purple. The greater the number of samples, the lighter the corresponding color in the matrix, and the closer it is to yellow. It can be seen from this confusion matrix that most of the sample data is concentrated in the diagonal position of the matrix, therefore, the vast majority of samples were correctly predicted.  
Table 3 shows the performance of six categories on the four indicators of accuracy--precision, recall, F1-score, and support. Among them, support represents the number of test samples in each category. It can be seen that the classification results obtained by the ELM model are at 91% accuracy.  
Table 4 shows the comparison of the extended ELM model and deep neural network (DNN) model. In terms of the network layers of the model, the extended ELM model has only five layers, so the model is simple. In terms of time consumption, extended ELM consumes less time than DNN, so the model runs very fast. In terms of prediction accuracy, the model accuracy of extended ELM model reaches 91%, so the model is very efficient.  
This section introduces the process of model tuning and the performance of the model. Consequently, extended ELM has the advantages of simplicity, fast running speed, and efficiency, as well as having broad development prospects.  
Figure 11: Confusion matrix of testing set.  
Figure 10: Influence of number of neurons in the hidden layer on the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Class 0 & 0.81 & 0.90 & 0.85 & 20,073 \\ Class 1 & 0.82 & 0.63 & 0.71 & 19,825 \\ Class 2 & 0.89 & 0.94 & 0.91 & 19,812 \\ Class 3 & 0.95 & 0.98 & 0.97 & 19,763 \\ Class 4 & 0.98 & 1.00 & 0.99 & 19,993 \\ Class 5 & 1.00 & 1.00 & 1.00 & 19,862 \\ Accuracy & & & 0.91 & 119,328 \\ Macro average & 0.91 & 0.91 & 0.90 & 119,328 \\ Weighted avg & 0.91 & 0.91 & 0.90 & 119,328 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of six categories on each metric.