## 4 Conclusions  
This study presented a BP-RNN solution that implements a two-stage neural network to summarize the 10-dimensional historical data, then extracted and reordered the features with a high weight to build up the bridge between the liquid-flow test and firing test for bipropellant thrusters. By using multidimensional nonlinear fitting in a BP neutral network through training and updating the weight, the association rule between the 10-dimension features and their mapping labels can be revealed. The NTO flow rate after injector welding and the SMD value were extracted with a high weight of 0.5052 and 0.8135, showing that these two features are the decisive hidden factor affecting the mixture ratio. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Set** & **Correct Classification** & **Classification of Deviations of 1** & **Accepted Prediction** \\ \hline Enhanced data & 55.31\% & 20.53\% & 75.84\% \\ Raw data & 54.89\% & 22.89\% & 77.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The condition prediction results of the mixing ratio classification. Figure 7: The curves of the classification loss (**a**) and accuracy (**b**) of training dataset and test dataset. According to two high-weight features and the final flow rate with an orifice of two flow channels, the many-to-one classification prediction was realized by using RNN. The percentage of accepted prediction could reach up to above 75% with a deviation not greater than 1. Even though the prediction accuracy of the complete classification was only around 55%, the result was still far higher than the current test qualified rate of about 30%. With the continuous increase in the number of bippellant thrusters tested in manufacturing, the training dataset can be further accumulated and improved for better prediction accuracy and generalization ability. After the accuracy of the prediction model reaches a high level, the liquid-flow test results can even be directly used for high-precision mixture ratio adjustment without the need for the firing test, thus meeting the low-cost engineering requirement. Furthermore, the prediction of specific working conditions on orbit can also be realized through the analysis of liquid-flow test data on the ground. Conceptualization, Z.Z. and Y.Y. ; methodology, Z.Z. ; software, Z.Z. ; validation, K.C. ; formal analysis, H.Y. ; investigation, Z.Z. ; resources, X.M. ; data curation, S.Y. ; writing--original draft preparation, Z.Z. ; writing--review and editing, Y.Y. ; visualization, F.W. ; supervision, Y.Y. ; project administration, Z.Z. ; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript. The research was supported by the Fundamental Research Funds for the Central Universities 2019 JBM055. Not applicable. The authors declare no conflict of interest. * _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Comparison of Chemical Propulsion Solutions for Large Space Debris Active Removal. In _Springer Aerospace Technology_; [PERSON] [PERSON], [PERSON] [PERSON], [PERSON], [PERSON]; Springer: Cham, Switzerland, 2017; pp. 985-1011. [CrossRef]
* _[PERSON] et al. (2003)_ [PERSON]; [PERSON]; [PERSON] _Introduction to Satellite Engineering_, 1 st ed. ; China Aerospace Press: Beijing, China, 2003; pp. 115-133. * _[PERSON] et al. (2006)_ [PERSON]; [PERSON]; [PERSON] The development of dual-mode chemical propulsion systems. _J. Rocket Propuls._**2006**, _32_, 31-37. * [PERSON] and [PERSON] (2005) [PERSON]; [PERSON] Simulation Analysis of Computer-Controlled Pressurization for Mixture Ratio Control. In Proceedings of the 41 st AIAA/ ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Arizona, AZ, USA, 10 July 2005. [CrossRef]
* _[PERSON] et al. (2013)_ [PERSON]; [PERSON]; [PERSON] Research on technology of regulating mixture ratio in propellant unified propulsion system. _Aerosp. Shanghai_**2013**, _30_, 51-55. [CrossRef]
* _[PERSON] et al.