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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Yuhui Wang,Wei Wang,Qingxian Wu.Winning Probability Estimation Based on Improved Bradley-Terry Model and Bayesian Network for Aircraft Carrier Battle[J].Journal of Harbin Institute Of Technology(New Series),2017,24(2):39-44.DOI:10.11916/j.issn.1005-9113.15254.
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Winning Probability Estimation Based on Improved Bradley-Terry Model and Bayesian Network for Aircraft Carrier Battle
Author NameAffiliation
Yuhui Wang College of Automation Engineering, Nanjing University of Aeronautics and AstronauticsNanjing 211106,China
Science and Technology on Electro-Optic Control Laboratory, Luoyang 471009, Henan, China
Jiangsu Key Laboratory of Internet of Things and Control Technologies, Nanjing 211106, China 
Wei Wang College of Automation Engineering, Nanjing University of Aeronautics and AstronauticsNanjing 211106,China 
Qingxian Wu College of Automation Engineering, Nanjing University of Aeronautics and AstronauticsNanjing 211106,China 
Abstract:
To provide a decision-making aid for aircraft carrier battle, the winning probability estimation based on Bradley-Terry model and Bayesian network is presented. Firstly, the armed forces units of aircraft carrier are classified into three types, which are aircraft, ship and submarine. Then, the attack ability value and defense ability value for each type of armed forces are estimated by using BP neural network, whose training results of sample data are consistent with the estimation results.Next, compared the assessment values through an improved Bradley-Terry model and constructed a Bayesian network to do the global assessment, the winning probabilities of both combat sides are obtained. Finally, the winning probability estimation for a navy battle is given to illustrate the validity of the proposed scheme.
Key words:  aircraft carrier battle  BP neural network  Bradley-Terry model  Bayesian networks
DOI:10.11916/j.issn.1005-9113.15254
Clc Number:TP397.1
Fund:
Descriptions in Chinese:
  

基于改进的Bradley-Terry模型和贝叶斯网络的

航母作战胜率评估

王玉惠1,2,3, 王玮1, 吴庆宪1

(1。南京航空航天大学 自动化学院; 2.光电控制技术重点实验室; 3.江苏省物联网与控制技术重点实验室)

创新点说明:

(1)针对传统Bradley-Terry模型不适用余航母作战单元比较的情况,提出了改进的Bradley-Terry模型,并用来比较航母作战双方的能力值;

(2)建立航母战全局评估的贝叶斯网络结构,从而评估其中一方获胜的概率。

研究目的:

航母作战时需要对比作战双方的交战实力,预先评估作战双方的获胜概率,所获评估结果将为指挥官提供辅助决策,但通过对已有成果的研究,我们发现关于战争结果预测的公开报道很少,而关于航母作战的结果预测更是未见报道。为此我们借鉴已有的动态过程预测成果,通过改进Bradley-Terry模型和构建全局贝叶斯网络,实现航母作战的胜率评估。

研究方法:

航母战的胜率评估主要分为以下3步:

第1步:将作战单元分类,基于BP神经网络评估出每一类作战单元的进攻能力值和防守能力值;

第2步:基于改进的Bradley-Terry模型进行双方的作战单元比较,然后得出一方作战单元战胜另一方作战单元的胜率;

第3步:基于Netica软件构建贝叶斯网络实现对航母战的胜率评估。

结果:

在第4节本文针对所提的方案给出了实例仿真,首先对作战单元进行分类(见第4节Step 1),然后基于BP神经网络进行作战单元能力评估(所得结果见表3),再次基于改进的Bradley-Terry模型进行作战单元比较(所得结果见表4),然后构建贝叶斯网络进行全局评估(见图5),通过对最终结果的对比可知,本文所提的方案更接近实际过程,从而验证了本文所提方案的有效性。

结论:

本文针对航母战的胜率评估问题研究了一种新的评估方案。论文的主要贡献在于为解决传统Bradley-Terry模型不能进行群对比的缺陷,提出了一种改进的Bradley-Terry模型。通过仿真实例可知,所提方案获得的结果明显优于专家判断。论文获得的成果不仅可为复杂战争的结果预测提供了一种新的解决方案,也可推广至其它复杂非线性动态过程的结果预测研究中。

关键词:航母战;BP神经网络;Bradley-Terry模型;贝叶斯网络

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