Prediction of Peak Overpressure of Underwater Cylindrical Charge Based on PSO-CNN-XGBoost

被引：0
作者：
Liu F. [1 ,2 ]
Li S. [3 ]
Lu X. [4 ]
Guo C. [4 ]
机构：
[1] School of Science, Shenyang Ligong University, Liaoning, Shenyang
[2] Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry, Shenyang Ligong University, Liaoning, Shenyang
[3] School of Automation and Electrical Engineering, Shenyang Ligong University, Liaoning, Shenyang
[4] School of Equipment Engineering, Shenyang Ligong University, Liaoning, Shenyang
来源：
Binggong Xuebao/Acta Armamentarii | 2024年 / 45卷 / 05期
关键词：
1D convolutional neural network; aspect ratio; blast distance; extreme gradient boosting; particle swarm optimization algorithm; peak overpressure; underwater cylindrical charge;
D O I：
10.12382/bgxb.2023.0743
中图分类号：
学科分类号：
摘要：
In order to explore the relationships among the peak overpressure of underwater cylindrical charge and the parameters of its structure and blast distance, the sample data of the charge is regarded as two-dimensional data. A peak overpressure fusion prediction algorithm of underwater cylindrical charge is established. The fusion prediction algorithm is based on particle swarm optimization(PSO)algorithm,1D convolutional neural network(1DCNN) and Extreme Gradient Boosting(XGBoost). Correlation analysis and data visualization methods are used to analyze the correlation among charge structure parameters, blast distance and peak overpressure. 1DCNN deep network is designed to mine the longitudinal temporal relationship among the parameters, including aspect ratio, blast distance, etc., and peak overpressure. XGBoost algorithm is applied to find the lateral nonlinear relationships among charge structure parameters, blast distance and peak overpressure, so as to improve the prediction accuracy of small sample data. PSO algorithm is used to optimize the hyperparameters of 1DCNN and XGBoost, and gain the optimal algorithm structure. In the comparison experiments involving ten intelligent algorithms, the accuracy, stability and fitting degree of PSO-CNN-XGBoost underwater cylindrical charge peak overpressure prediction algorithm are higher than those of other algorithms. © 2024 China Ordnance Industry Corporation. All rights reserved.
引用
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页码：1602 / 1612
页数：10
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