Modern Defense Technology ›› 2025, Vol. 53 ›› Issue (2): 45-54.DOI: 10.3969/j.issn.1009-086x.2025.02.005

• AIR SPACE DEFENSE SYSTEM AND WEAPON • Previous Articles     Next Articles

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

Haiyan ZHAO1,2, Feng ZHOU1, Wenjing YANG2, Di LIU1,3, Tianyuan YANG1   

  1. 1.Air Defense and Antimissile School,Air Force Engineering University,Xi′an 710051,China
    2.College of Information and Communication,National University of Defense Technology,Wuhan 430035,China
    3.Army Border and Coastal Defense Academy,Xi′an 710108,China
  • Received:2024-06-03 Revised:2024-08-08 Online:2025-04-28 Published:2025-04-30
  • Contact: Feng ZHOU

基于GWO-DBN的反导装备体系效能评估方法研究

赵海燕1,2, 周峰1, 杨文静2, 刘迪1,3, 杨添元1   

  1. 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
  • 通讯作者: 周峰
  • 作者简介:赵海燕(1978-),女,山西侯马人。副教授,博士,研究方向为反导装备体系效能评估。
  • 基金资助:
    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)

Abstract:

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.

Key words: anti-missile equipment system, effectiveness evaluation, data-driven, deep learning, grey wolf optimization(GWO), deep belief network(DBN)

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。

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)

CLC Number: