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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 506))

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Abstract

A cost estimation method for launch vehicles is proposed combining the concepts of machine learning, aims to provide assistance in strategic decision-making processes pertaining to satellite launch activities. First, the characteristics of existing methods for estimating the cost of launch vehicles are analyzed, and draws out the machine learning methods based on the characteristics of the current development of launch vehicles in China. Next, a model algorithm based on a dynamic neural network and grey relational analysis is introduced. This algorithm simplifies the network structure by iteratively eliminating low correlation coefficient nodes, effectively addressing the issue of overfitting in small sample data. Finally, the proposed method is validated through a case study about prediction of the Long March series launch vehicle, demonstrating its feasibility and effectiveness.

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Notes

  1. 1.

    Data source URL:

    1. 1.

      http://www.spaceflightfans.cn.

    2. 2.

      https://www.spacex.com.

    3. 3.

      http://www.spacechina.com.

    4. 4.

      https://www.heavens-above.com.

  2. 2.

    Data source URL:

    1. 1.

      http://www.spacechina.com.

    2. 2.

      https://www.cnsa.gov.cn.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants (71971213, and 72301288), and by the General Project of Postgraduate Scientific Research Innovation Project of Hunan Province (Grant No. CX20230086).

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Correspondence to Bingfeng Ge .

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Liu, Z., Ge, B., Huang, Y., Hou, Z., Wei, W., Li, J. (2024). Research on Cost Estimation of Launch Vehicle Based on Grey Neural Network. In: Duarte, S.P., Lobo, A., Delibašić, B., Kamissoko, D. (eds) Decision Support Systems XIV. Human-Centric Group Decision, Negotiation and Decision Support Systems for Societal Transitions. ICDSST 2024. Lecture Notes in Business Information Processing, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-59376-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-59376-5_4

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