基于改进Stacking集成学习方法的武器装备体系作战效能预测

doi:10.12382/bgxb.2022.0797

摘要/Abstract

摘要：

作战效能预测对武器装备体系从建设、生产到实战的全过程都具有重要意义。在Stacking集成学习模型的基础上,优化模型对数据的交叉验证方式,针对原有模型次级学习器输入向量较为稀疏的问题,为次级学习层的输入增加多项式特征和经主成分分析法降维后的各项作战仿真数据指标(原始数据),形成一种改进Stacking集成学习模型的装备体系作战效能预测方法。以合成营攻占某一阵地的作战效能预测为例,验证该方法的有效性。

关键词: 武器装备体系, Stacking集成学习, 机器学习, 作战效能预测, 要点夺控

Abstract:

Operational effectiveness prediction is a great significance to the weapon equipment system in the whole process from construction, production to actual combat. The cross-validation method for data is optimized based on the Stacking ensemble learning model. For the problem of sparse input vector of secondary learner in the original model, the input polynomial characteristics and the combat simulation data indicators (raw data) after PCA dimension reduction are increased to the learning layer, A prediction method for the operational effectiveness of equipment system with improved Stacking ensemble learning model is proposed. The effectiveness of the method is verified by an example of the operational effectiveness prediction of a synthetic battalion seizing a position.

Key words: weapon equipment system, Stacking ensemble learning, machine learning, operational effectiveness prediction

中图分类号:

TP181

李驰运, 缪建明, 沈丙振. 基于改进Stacking集成学习方法的武器装备体系作战效能预测[J]. 兵工学报, 2023, 44(11): 3455-3464.

LI Chiyun, MIAO Jianming, SHEN Bingzhen. Operational Effectiveness Prediction of Weapon Equipment System Based on Improved Stacking Ensemble Learning Method[J]. Acta Armamentarii, 2023, 44(11): 3455-3464.

图/表 13

图1 传统Stacking模型

Fig.1 Traditional Stacking model

图2 改进Stacking模型

Fig.2 Improved Stacking model

表1 要点夺控体系作战实体组成

Table 1 Composition of main point capture and control offense and defense system against entities

对抗系统	名称
情报侦察系统	某型侦察雷达、某型无人侦察机(有无)
指挥控制系统	指挥车、短波电台等
远程火力打击系统	某型自行迫榴炮系统、某型导弹发射车系统等
防空系统	某型高炮、某型便携式防空导弹等
近战打击系统	Ⅰ型主战坦克、Ⅱ型主战坦克等
综合保障系统	清障车、弹药输送车等

表2 要点夺控合成营攻防体系仿真主要作战配置

Table 2 Main combat configuration table of the simulation of attack and defense system of the key capture and control synthetic battalion

配置类型	因素	水平
	近战突击装备	Ⅰ型主战坦克,Ⅱ型主战坦克
装备配置	远程火力打击	某自行迫榴炮系统,某导弹发射车系统
	防空营装备	某型高炮,某型便携式防空导弹
	侦察装备	某型无人侦察机(有无)
	近战进攻战法	分散突击,集中目标
战术战法	近战战斗队形	一字队形,前三甲队形,后三角队形
	远程火力战法	急袭射击,直瞄破坏

表3 基础指标样本数据

Table 3 Sample data of basic indicators

样本号	指标1	指标2	…	指标24	作战效能值
1	3	0.7		50	1
2	1	0.6		70	0
3	2	0.8		40	0
4	3	0.5		80	1
︙	︙	︙	…	︙	︙
9600	2	0.6		50	0

表4 各类算法训练准确率指标

Table 4 Training accuracy index table of various algorithms

模型	单一KNN	单一SVM	单一XGBoost	传统Stacking	改进Stacking
准确率(Accuracy)	0.6690	0.6822	0.7674	0.8560	0.9107
ROC曲线下面积总和(AUC)	0.6332	0.6647	0.7328	0.8229	0.9019

图3 多种模型对比MSE均方误差图

Fig.3 Comparison of MSEs of various models

图4 多种模型ROC曲线对比图

Fig.4 Comparison of ROC curves of various models

图5 XGBoost模型预测图

Fig.5 Prediction diagram of XGBoost model

图6 SVM模型预测图

Fig.6 Prediction diagram of SVM model

图7 KNN模型预测图

Fig.7 Prediction diagram of KNN model

图8 传统Stacking模型预测图

Fig.8 Prediction diagram of traditional Stacking model

图9 改进Stacking模型预测图

Fig.9 Prediction diagram ofimproved Stacking model

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