摘要
针对传统效能评估方法难以体现反导装备体系的演化性、涌现性和自适应性等问题,提出了一种基于数据驱动的反导装备体系效能评估方法。在分析反导装备体系特点和传统效能评估方法不足的基础上,采用贝叶斯优化算法对卷积神经网络超参数进行优化,构建了贝叶斯卷积神经网络效能评估模型;研究了贝叶斯卷积神经网络反导装备体系效能评估算法流程、步骤,形成一套完成的反导装备体系效能评估算法;设计验证仿真实验,输入大量试验数据对贝叶斯卷积神经网络模型进行训练和学习,以获得对反导装备体系效能的仿真预测。实验结果表明:数据驱动下的反导装备体系效能评估拟合度较高,期望输出结果与实际输出结果之间的差距非常小,该方法具有较高的可行性和可信性。
Aiming at the problems that traditional effectiveness evaluation methods can not reflect the evolution,emergence and adaptability of the anti-missile equipment system,a data-driven effectiveness evaluation method of anti-missile equipment system was proposed.Based on the analysis of the characteristics of anti-missile equipment system and the shortage of traditional effectiveness evaluation method.the Bayes optimization algorithm was used to optimize the convolutional neural network hyperparameters,and the efficiency evaluation model of Bayes-CNN(Bayes convolutional neural network)was constructed.The flow and steps of Bayes-CNN system effectiveness evaluation algorithm were studied,and a set of completed efficiency evaluation algorithm was formed.Designed and validated the simulation experiment,input a lot of test data to Bayes-CNN model for training and learning,so as to obtain the simulation prediction of the effectiveness of anti-missile equipment system.The experimental results show that the error between the actual and expected output is very small,and the non-linear fitting effect is great so that it had a high degree of feasibility and reliability.
作者
赵海燕
周峰
杨文静
赵静
王小双
ZHAO Haiyan;ZHOU Feng;YANG Wenjing;ZHAO Jing;WANG Xiaoshuang(Air Defense and Antimissile School,Air Force Engineering University,Xi'an 710051,China;College of Information and Communication,Wuhan 430035,China)
出处
《国防科技大学学报》
北大核心
2025年第3期81-89,共9页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(62001059)
陕西省自然科学基础研究计划面上资助项目(2023JCYB509)。
作者简介
第一作者:赵海燕(1978—),女,山西侯马人,副教授,博士研究生,E-mail:813086903@com;通信作者:周峰(1973—),男,安徽霍邱人,教授,博士,博士生导师,E-mail:zzzfff00@163.com。
