# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 6 Experiment
### Analysis of Results  
The experiment compares the planning profit and planning time of the five algorithms, CPLEX benchmark algorithm, PF, d-PSB, GBDT, and SRNN-HS (Symmetric Recurrent Neural Network Heuristic Search algorithm) in terms of the global urban datasets [38; 39; 40]. Table 2 shows the planned profit and profit gap for each algorithm, Figure 5 shows the revenue error for each algorithm, Table 3 and Figure 6 show the calculation time for each algorithm. \begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Parameter Value** \\ \hline Semimajor axis & 7065 km \\ Semimajor axis & 0.0000923 km \\ Orbital inclination & 98.7359 \\ Right ascension point & 183.922 \\ Perigee amplitude & 114.442 km \\ Angle of approach & 354.784 \\ \hline \end{tabular}
\end{table} 
Table 1: Track parameter setting. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Indicator** & **CPLEX** & **PF** & **d-PSB** & **GBDT** & **SRNN-HS** \\ \hline
100 & profit & 1577.90 & 1559.17 & 1518.50 & 1295.70 & 1540.0 \\  & profit gap & & 1.73\% & 3.76\% & 17.91\% & 2.40\% \\  & profit & 2303.20 & 2126.83 & 2148.80 & 2072.90 & 2225.50 \\
200 & profit gap & & 7.1\% & 6.76\% & 10.00\% & 3.36\% \\  & profit gap & & 2391.77 & 2551.40 & 2526.60 & 2672.10 \\
300 & profit gap & & 12.46\% & 7.46\% & 08.32\% & 3.06\% \\
400 & profit & 3096.50 & 2521.13 & 2866.50 & 2732.70 & 3012.40 \\  & profit gap & & 17.87\% & 7.43\% & 11.73\% & 2.72\% \\
500 & profit & 3378.70 & 2610.70 & 3105.80 & 2915.00 & 3262.60 \\  & profit gap & & 21.70\% & 8.06\% & 13.72\% & 3.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental result. Figure 5: Profit gap comparison chart. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **PF** & **d-PSB** & **GBDT** & **SRNN-HS** \\ \hline
100 & 0.00038 s & 0.00041 s & 0.00030 s & 0.00068 s \\
200 & 0.00047 s & 0.00058 s & 0.00030 s & 0.00077 s \\
300 & 0.00051 s & 0.00071 s & 0.00027 s & 0.00081 s \\
400 & 0.00055 s & 0.00079 s & 0.00024 s & 0.00085 s \\
500 & 0.00056 s & 0.00082 s & 0.00020 s & 0.00086 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation time. The calculation result of the CPLEX benchmark algorithm is the optimal scheme for satellite task planning, but its calculation time is unacceptable for the on-board computer, so it can only be used as a benchmark algorithm for evaluating the pros and cons of the algorithm. The profit gap indicates a gap with CPLEX, and the smaller the value, the better the planning effect. As can be seen from Table 2, the SRNN-HS proposed in this paper has achieved the highest task planning benefits in most scenarios, indicating the effectiveness of SRNN-HS. As can be seen from Table 2 and Figure 5, in the planning scenario s100, the PF is larger in planning revenue than other algorithms, because the neural network is required to calculate the schedulable probability of each task. However, as can be seen, the calculation time increment is not very large. Even if the performance of the onboard computer is only 1/100 of that of an office computer, our algorithm can still give the planning result in a short time. From Figure 1, it can be seen that in the planning scenario s100, PF has a less significant planning revenue gap than that of other algorithms. This is because the number of observation tasks involved in planning is lower, the degree of conflict between tasks is lower, and the selection of observation tasks with a high revenue value can achieve better results. Except for the planning scenario, s100, the SRNN-HS algorithm has less revenue error than the three heuristic algorithms, PF, d-PSB, and GBDT. This is because the task scheduling of the heuristic algorithms is obtained by researchers based on time experience. Due to the existence of blind spots in knowledge or difficulties in knowledge representation, there is still room for improvement in the optimization of rule-based heuristic search algorithms. It can be seen that SRNN-HS is also better than the GBDT algorithm.