## 5 Discussion  
The flight data and physiological indexes in the simulation experiment were sorted out and analyzed. Here, the time-varying characteristics of intention and human memory characteristics are comprehensively considered, and a change-of-intention curve (measured value) was drawn. The characteristic data of the experimental samples are input into the pilot intention identification model, and the intention identification simulation experiment is carried out. According to the real-time identification results of the model, the change of the pilot's intention curve (simulated value) is drawn and the comparison between the simulated result and the measured value is obtained. The verification results of a simulation experiment are revealed in Figure 9. It can be seen from the results of the simulation experiments that the identification results of the model proposed in this paper have a high degree of fit with the pilot's measured values, and the accuracy of identification is about 90%. To further verify and evaluate the stability and reliability of the CSA-ELM model, the traditional BP, ELM, and support-vector machine models were used to process the physiological data during the simulated flights of 32 pilots. The identification results of these methods are compared with the target intention identified by CSA-ELM, and the intention identification accuracy rate of each method is described in Figure 10.  
Figure 10 is a comparison diagram of the accuracy of the four methods for pilot intention identification. The identification accuracy of the CSA-ELM model is 87.2%, which is 18.5%, 8.1%, and 17.7% higher than BP, ELM, and SVM, respectively. The results show that CSA-ELM has higher accuracy and better stability for pilot intention identification. Moreover, it can better process multiparameter time-series data. Figure 11 shows the percentage of the same number of identification results when four methods are used to identify the same pilot's intentions. Figure 11 shows the percentage of the same number of identification results when four methods are used to identify the same \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metrics** & **Training Set** & **Testing Set** \\ \hline Precision rate & 0.8256 & 0.8226 \\ Recall rate & 0.8341 & 0.8422 \\ Accuracy rate & 0.8673 & 0.8452 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of the CSA-ELM model. Figure 9: Comparison chart of a pilot’s intention simulation value and measured value. pilot's intention. It can be seen from Figure 11 that the same degree of the four methods is 86.7% in identifying whether the pilot's intention is generated. It can be proved that the proposed intention identification model has high reliability. In this study, based on the physiological indexes such as ECG, RESP, and EDA obtained by simulating airfield traffic patterns, the results show that physiological indexes such as ECG, RESP, and EDA can be used for intention identification. This is consistent with the results of other relevant pilot micro-cognition studies [22; 23; 24]. Compared with previous studies [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27], the main advantages of this study are reflected in the following aspects. (1) In this study, a large number of airfield traffic-pattern simulation experiments were carried out, and the amount of experimental data was larger and more reliable. (2) The ESAX feature extraction method is utilized in this paper; it can obtain more comprehensive feature variables from experimental data. The extracted characteristic variables are used to identify the pilot's intentions. The results of this research enrich the research of the characteristic indicators of micro-cognitive behaviors such as pilot intention. (3) The established CSA-ELM identification model based on characteristic ESAX variables has a higher accuracy. The accuracy of intention recognition is as high as 87.2%, which is 18.5%, 8.1%, and 17.7% higher than BP, ELM, and SVM, respectively.