## 4 Experiments and Analysis
### Real-Time and Popularization Analysis of Model  
The experiment was carried out using the Windows 10 system, the central processing unit was a 2.90 GHz i5 processor with a memory of 32.0 GB, and the experiment was carried out with MATLAB2021b. In the comparison experiment of this paper, the initial population of the GA algorithm was set to 40, and the maximum number of iterations was 100. The training time of the network and the execution time after training were used to compare and discuss the real-time analysis and generalization of the network. The experimental results are shown in Table 3.  
From the comparative analysis in Table 3, we can see that the methods adopted in the experiment require a certain amount of time to construct the network, and the difference in network structure makes the time used for network training different. It can be seen from Table 3 that: 1 In the network training stage, the training times of the LSTM method and the SVM method are equivalent; after adding GA, the time consumption of the network training stage is increased by about two times; 2 In the network execution stage, the time consumptions of the LSTM and SVM methods are basically the same. Compared with the other two algorithm models, the time consumption of the GA-LSTM method is not significantly increased but decreases slightly. In the MATLAB2021b environment, the execution time of GA-LSTM is about 0.2 s.  
It can be seen that in practical applications, except for about 3 min in the program start-up training phase, the ship trajectory prediction results can be obtained quickly in other time periods. If another compiled language such as C writes the algorithm into the hardware to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model** & **Position** & **MSE** & **MAE** & \multicolumn{2}{c}{
\begin{tabular}{c} **Optimal Paramter** \\ **Combination** \\ \end{tabular} } \\ \hline  & & & & & **Num of** & **Learning** \\  & & & & & **Neuron** & **Rate** \\ \hline ship-1 & SVM & LAT & \(9.979\times 10^{-6}\) & 0.002 & & & \\  & & LON & \(1.4957\times 10^{-5}\) & 0.0034 & & & \\  & LSTM & LAT & \(2.6257\times 10^{-6}\) & 0.0015 & & & \\  & & LON & \(7.8145\times 10^{-6}\) & 0.0026 & & & \\  & GA-LSTM & LAT & \(1.6393\times 10^{-6}\) & 0.0014 & 11 & 0.0165 \\  & & LON & \(4.3188\times 10^{-6}\) & 0.0024 & 7 & 0.0230 \\ \hline ship-2 & SVM & LAT & \(1.3404\times 10^{-5}\) & 0.0035 & & & \\  & & LON & \(1.0037\times 10^{-5}\) & 0.0029 & & & \\  & LSTM & LAT & \(5.4005\times 10^{-6}\) & 0.0023 & & & \\  & & LON & \(8.742\times 10^{-6}\) & 0.0027 & & & \\  & GA-LSTM & LAT & \(3.0375\times 10^{-6}\) & 0.0017 & 13 & 0.0163 \\  & & LON & \(1.8304\times 10^{-6}\) & 0.0012 & 14 & 0.0105 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance index analysis of models on 3 models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & \multicolumn{2}{c}{**Training Time T/S**} & \multicolumn{2}{c}{**Execution Time T/S**} \\ \hline  & & **LON** & **LAT** & **LON** & **LAT** \\ \hline ship-1 & SVM & 55.915569 & 56.1377 & 0.426141 & 0.424251 \\  & LSTM & 53.9218 & 53.766166 & 0.403474 & 0.404457 \\  & GA-LSTM & 154.213806 & 166.647849 & 0.129239 & 0.129546 \\ \hline ship-2 & SVM & 56.137049 & 56.586795 & 0.427225 & 0.433015 \\  & LSTM & 53.671202 & 53.841451 & 0.400593 & 0.401865 \\  & GA-LSTM & 145.977239 & 142.860358 & 0.117013 & 0.123151 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of real-time analysis of 3 models.  
run, the running speed will be further improved. Therefore, the method proposed in this paper can meet the needs of certain scenarios in terms of real-time performance and has a good generalization.