基于AGEP-DNN的水下聚能装药比冲量预测模型
Prediction Model of Specific Impulse of Underwater Shaped Charge Based on AGEP-DNN
刘芳 1郝慧敏 2卢熹 3郭策安3
作者信息
- 1. 沈阳理工大学 理学院,沈阳 110159;辽宁省兵器工业智能优化与控制重点实验室,沈阳 110159
- 2. 沈阳理工大学 自动化与电气工程学院,沈阳 110159
- 3. 沈阳理工大学 装备工程学院,沈阳 110159
- 折叠
摘要
聚能装药比冲量是表征水下爆炸中冲击波对目标破坏作用的重要参数.为实现水下聚能装药比冲量智能预测,提出一种自适应基因表达式编程(adaptive gene expression program-ming,AGEP)优化深度神经网络(deep neural network,DNN)的聚能装药比冲量预测模型(AGEP-DNN).考虑装药结构与比冲量数值之间的复杂非线性关系,通过AUTODYN软件建立有限元模型,对水下爆炸过程进行仿真,采用经验公式验证仿真数据的有效性;基于仿真实验数据,设计AGEP算法优化DNN超参数,构建AGEP-DNN模型,对比冲量进行智能预测.实验结果显示,AGEP-DNN聚能装药比冲量预测模型在 9 种对比智能预测模型中具有最优的预测精度.
Abstract
The specific impulse of shaped charge is an important parameter in underwater explo-sions.It is used to represent the destructive effect of a shock wave on a target.In order to predict the specific impulse,a prediction model of shaped charge based on gene expression programming optimized deep neural network(AGEP-DNN)is proposed.Considering the complex nonlinear rela-tionship between the structure and the specific impulse value of the charge,AUTODYN is applied to build finite element models.Empirical formulas are used to validate the data.Based on the simu-lation experimental data,an adaptive gene expression programming(AGEP)is designed to optimize deep natural network(DNN)hyperparameters.The AGEP-DNN model is constructed to intelligently predict the specific impulse.Experimental results show that among the nine prediction models,AGEP-DNN has the highest accuracy.
关键词
聚能装药/比冲量/自适应基因表达式编程/深度神经网络/数值仿真Key words
shaped charge/specific impulse/adaptive gene expression programming/deep natural network/numerical simulation引用本文复制引用
基金项目
辽宁省教育厅高等学校基本科研项目(LJKMZ20220619)
出版年
2024
NETL
NSTL
万方数据