基于GWO-DBN的反导装备体系效能评估方法研究  

Research on Effectiveness Evaluation Method of Anti-missile Equipment System Based on GWO-DBN

作　　者：赵海燕 周峰[1] 杨文静 刘迪 杨添元 ZHAO Haiyan;ZHOU Feng;YANG Wenjing;LIU Di;YANG Tianyuan(Air Defense and Antimissile School,Air Force Engineering University,Xi′an 710051,China;College of Information and Communication,National University of Defense Technology,Wuhan 430035,China;Army Border and Coastal Defense Academy,Xi′an 710108,China)

机构地区：[1]空军工程大学防空反导学院,陕西西安710051 [2]国防科技大学信息通信学院,湖北武汉430035 [3]陆军边海防学院,陕西西安710108

出　　处：《现代防御技术》2025年第2期45-54,共10页Modern Defence Technology

基　　金：国家自然科学基金(62001059);陕西省自然科学基础研究计划面上项目(2023JCYB509)。

摘　　要：针对现有效能预测方法难以反映反导装备体系实际效能的问题,提出一种基于“数据驱动+深度学习”的反导装备体系效能评估方法。在大量实验数据抽取、处理、分析的基础上,构建灰狼优化算法-深度置信网络(GWO-DBN)模型对数据进行训练学习,以此获得反导装备体系效能的非线性拟合,并以某次反导体系效能评估为例进行了仿真实验。结果表明,该评估方法可行、可靠,能够为反导装备体系论证和改进提供较高的参考价值和借鉴意义。Aiming at the problem that the existing efficiency prediction methods are difficult to reflect the actual effectiveness of anti-missile equipment system,a method of efficiency evaluation of anti-missile equipment system based on"data-driven+deep learning"is proposed.On the basis of a large number of experimental data extraction,disposal and analysis,we construct grey wolf optimization(GWO)-deep belief network(DBN)model to train the data,so as to obtain the nonlinear fitting of the anti-missile equipment system efficiency.We conduct a simulation experiment with an anti-missile system efficiency evaluation as an example,and the results show that the evaluation method is feasible and reliable.It can provide high reference value and significance for the demonstration and improvement of the anti-missile equipment system.

关 键 词：反导装备体系 效能评估 数据驱动 深度学习 灰狼优化算法(GWO) 深度置信网络(DBN) 

分 类 号：E917[军事] TJ761.7[兵器科学与技术—武器系统与运用工程] TP18[自动化与计算机技术—控制理论与控制工程]