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Dynamic response prediction of underwater explosive vessel based on LOO-XGBoost model

  • S.I.: Evolutionary Computation based Methods and Applications for Data Processing
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Abstract

For the characteristics of the dynamic response real test data of underwater explosive vessels with few feature dimensions, unclear feature relationships and small effective data amount, to improve the prediction precision of the dynamic response of the container, a dynamic response prediction model based on the LOO-XGBoost algorithm is proposed. The model uses a CART tree as the base learner, inputs the preprocessed data, and trains the target model layer by building multiple weak learners. Compared with the prediction models based on LOO-SVR, 10FLOD-XGBoost and BPNN, the simulation performance is better, the prediction accuracy is higher, and it has the significant advantage of avoiding the standardization of data features and not caring about whether the features are inter-dependent. It provides certain feasibility for the statistical prediction of the small sample capacity of similar projects.

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Data availability statement

Data are available on request from the authors.

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Acknowledgements

This work was supported by Hubei Key Laboratory of Blasting Engineering Foundation under Grant No. HKLBEF202009.

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Authors and Affiliations

  1. College of Science, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China

    Linna Li, Jun Gu & Dongwang Zhong

  2. Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan, 430065, Hubei, China

    Linna Li & Dongwang Zhong

  3. Hubei Key Laboratory of Blasting Engineering, Wuhan, 430056, Hubei, China

    Xiaowu Huang

  4. Wuhan Blasting Co., Ltd., Wuhan, 430030, Hubei, China

    Xiaowu Huang

Authors
  1. Linna Li
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  2. Jun Gu
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  4. Dongwang Zhong
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Correspondence to Linna Li.

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Li, L., Gu, J., Huang, X. et al. Dynamic response prediction of underwater explosive vessel based on LOO-XGBoost model. Neural Comput & Applic 35, 25057–25067 (2023). https://doi.org/10.1007/s00521-023-08613-x

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  • DOI: https://doi.org/10.1007/s00521-023-08613-x

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