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Neural Computing and Applications

Erschienen in:

06.07.2023 | S.I.: Evolutionary Computation based Methods and Applications for Data Processing

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

verfasst von: Linna Li, Jun Gu, Xiaowu Huang, Dongwang Zhong

Erschienen in: Neural Computing and Applications | Ausgabe 36/2023

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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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Titel: Dynamic response prediction of underwater explosive vessel based on LOO-XGBoost model
verfasst von: Linna Li
Jun Gu
Xiaowu Huang
Dongwang Zhong
Publikationsdatum: 06.07.2023
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 36/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-023-08613-x

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