摘要
为探索水下柱形装药结构、爆距等参数与水下柱形装药峰值超压的关系,将装药样本数据视为二维数据,建立粒子群优化(Particle Swarm Optimization,PSO)算法、一维卷积神经网络(1D Convolutional Neural Network,1DCNN)和极端梯度提升(Extreme Gradient Boosting,XGBoost)的水下柱形装药峰值超压融合预测算法。采用相关性分析与数据可视化方法,分析装药结构参数、爆距与峰值超压之间的关联关系。设计1DCNN深度网络挖掘不同长径比、爆距等参数与峰值超压之间的纵向时序关系。运用XGBoost算法寻找装药结构参数、爆距与峰值超压之间的横向非线性关系,提升小样本数据的预测精度。使用PSO算法优化1DCNN和XGBoost的超参数,获得最优算法结构。研究结果表明,在包含10种智能算法的对比实验中,PSO-CNN-XGBoost水下柱形装药峰值超压预测算法在精度、稳定性、拟合程度上均高于其他模型。
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.
作者
刘芳
李士伟
卢熹
郭策安
LIU Fang;LI Shiwei;LU Xi;GUO Ce an(School of Science,Shenyang Ligong University,Shenyang 110159,Liaoning,China;Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry,Shenyang Ligong University,Shenyang 110159,Liaoning,China;School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,Liaoning,China;School of Equipment Engineering,Shenyang Ligong University,Shenyang 110159,Liaoning,China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2024年第5期1602-1612,共11页
Acta Armamentarii
基金
辽宁省教育厅基本科研项目(LJKMZ20220619)。
关键词
水下柱形装药
长径比
爆距
峰值超压
粒子群优化算法
一维卷积神经网络
极端梯度提升
underwater cylindrical charge
aspect ratio
blast distance
peak overpressure
particle swarm optimization algorithm
1D convolutional neural network
extreme gradient boosting
