摘要
针对装备体系效能预测问题,提出了基于极端梯度提升(extreme gradient boosting,XGBoost)算法的装备体系效能预测方法。首先给出了基于XGBoost的效能预测流程;然后利用XGBoost建立评估指标与效能之间的非线性映射模型,实现对装备体系效能的预测。模型构建过程中采用了格栅搜索确定模型最优参数,避免了人为设定的盲目性。以某防空导弹武器系统效能预测为例,验证了该方法的有效性。
Aiming at the problem of equipment system-of-systems effectiveness prediction,an equipment system-ofsystems effectiveness prediction method based on extreme gradient boosting(XGBoost)is proposed.First,the effectiveness prediction process based on XGBoost is given.Then XGBoost is used to establish a nonlinear mapping model between these indicators and effectiveness to realize the prediction of equipment system-of-systems effectiveness.In the process of model construction,the grid search is used to determine the optimal parameters of the model,which avoids the blindness of artificial setting.Taking the effectiveness prediction of a certain air-defense missile weapon system as an example,the above-proposed method is validated.
作者
祝颂
钱晓超
陆营波
刘飞
ZHU Song;QIAN Xiaochao;LU Yingbo;LIU Fei(School of Software Engineering,South China University of Technology,Guangzhou 510006,Guangdong,China;Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China)
出处
《空天防御》
2021年第2期1-6,共6页
Air & Space Defense
基金
装备预研领域基金(61400010205)
上海航天科技创新基金(SAST2018-010)。
关键词
极端梯度提升
机器学习
效能预测模型
格栅搜索
防空导弹
extreme gradient boosting(XGBoost)
machine learning
effectiveness prediction model
grid search
air-defense missile
