Operational Effectiveness Prediction of Weapon Equipment System Based on Improved Stacking Ensemble Learning Method

doi:10.12382/bgxb.2022.0797

Abstract

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

CLC Number:

TP181

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.

Figures/Tables 13

References 25

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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

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

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

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

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