现代防御技术 ›› 2021, Vol. 49 ›› Issue (4): 15-23.DOI: 10.3969/j.issn.1009-086x.2021.04.003

• 空天防御体系与武器 • 上一篇    下一篇

决策树集成方法在反舰导弹效能评估中的应用

姬正一1, 陈阳2, 沈培志3, 韩先平1, 齐鸿坤3   

  1. 1.中国人民解放军92941部队,辽宁 葫芦岛 125000;
    2.中国人民解放军92493部队,辽宁 葫芦岛 125000;
    3.海军航空大学,山东 烟台 264001
  • 收稿日期:2020-08-06 修回日期:2021-01-10 出版日期:2021-08-20 发布日期:2021-09-06
  • 作者简介:姬正一(1986-),男,河南南阳人。助工,硕士生,主要从事海军兵种战术研究。通信地址:264001 山东省烟台市芝罘区二马路188号研一队 E-mail:514785482@qq.com

Decision Tree Integration Method Application in Effectiveness Evaluation of Anti-Ship Missile Weapon System

JI Zheng-yi1, CHEN Yang2, SHEN Pei-zhi3, HAN Xian-ping1, QI Hong-kun3   

  1. 1. PLA,No.92941 Troop,Liaoning Huludao 125000,China;
    2. PLA,No.92943 Troop,Liaoning Huludao 125000,China;
    3. Naval Aviation University,Shandong Yantai 264001,China
  • Received:2020-08-06 Revised:2021-01-10 Online:2021-08-20 Published:2021-09-06

摘要: 在反舰导弹效能评估方法中,针对存在主观经验和计算时间成本高的问题,提出了随机森林和梯度提升回归树2种决策树集成方法。通过构建3层19个分量的反舰导弹效能评估指标体系,改进的ADC(availability dependability capacity)评估模型建立了不同状态反舰导弹武器系统参数数据样本240份,切分数据集后采用归一化处理,结合交叉验证和网格搜索等参数优化方法,得到了2个较为理想的决策树集成效能评估模型。在仿真试验测试验证中,模型的评估准确率较高,验证了该方法的实用性,为反舰导弹效能评估提供了新思路。

关键词: 反舰导弹, 效能评估, 决策树集成, 随机森林, 梯度提升回归树, 机器学习

Abstract: Methods based on random forest and gradient boosting regression tree are proposed in order to improve the problem of subjective experience and high cost of calculation time in the effectiveness evaluation of anti-ship missile.By constructing a 3-layer and 19-component anti-ship missile effectiveness evaluation index system,240 operational parameter data samples of anti-ship missile in different states are set up by the improved ADC evaluation model,then the data set is segmented and normalized,combining with cross validation and grid search,two ideal decision tree integration effectiveness evaluation models are obtained.In the simulation test,the accuracy of the two models is high,which proves the practicability of the method and provides new ideas for anti-ship missile effectiveness evaluation.

Key words: anti-ship missile, effectiveness evaluation, decision tree integration, random forest, gradient boosting regression tree, machine learning

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