摘要
针对陆空联合作战效能评估问题,构建了具有6个二级评估指标和18个三级评估指标的综合评估指标体系。利用BP神经网络的度量方法,对三级指标进行评估,有效降低了变量个数。采用贝叶斯网络的度量方法,通过专家确定网络结构,利用蒙特卡罗算法确定条件概率表CPT,建立了陆空联合作战的综合效能评估模型,有效克服了AHP法和贝叶斯网络单独进行作战效能评估的不足。仿真结果说明,该评估模型性能良好,具有一定的科学性和可操作性。
In order to solve the effectiveness evaluation problem of air-ground joint operations,this essay constructs with the comprehensive evaluation index system that has six secondary and eighteen tertiary evaluation index.The measurement method which evaluates tertiary index effectively reduce the number of variables through using the BP neural network.And the measurement method establishes the comprehensive effectiveness evaluation model of air-ground joint operations through using Bayesianes networks.The network structure is determined by the expert,and a Montecarlo algorithm is used to determine the conditional probability table CPT.This method effectively overcomes the disadvantage of the AHP and the Bayesian networks method for operational effectiveness assessment alone.The simulation results show that the model performance is good,and the model has certain scientific nature and operability.
作者
周兴旺
从福仲
庞世春
ZHOU Xing-wang;CONG Fu-zhong;PANG Shi-chun(Unit 95519 of PLA,Zunyi,563000,China;Department of Basic,Avaition University of Air Force,Changchun 130000,China)
出处
《火力与指挥控制》
CSCD
北大核心
2018年第4期3-8,共6页
Fire Control & Command Control
基金
国家自然科学基金资助项目(11171350)
关键词
联合作战
效能评估
BP神经网络
贝叶斯网络
蒙特卡罗
joint operations
effectiveness assessment
BP neural network
Bayesian networks
Montecarlo
