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计算机工程 ›› 2020, Vol. 46 ›› Issue (9): 283-291,297. doi: 10.19678/j.issn.1000-3428.0055544

• 开发研究与工程应用 • 上一篇    下一篇

基于自适应谐振理论的武器目标分配快速决策算法

张凯, 周德云, 杨振, 潘潜   

  1. 西北工业大学 电子信息学院, 西安 710072
  • 收稿日期:2019-07-22 修回日期:2019-09-28 发布日期:2019-10-10
  • 作者简介:张凯(1988-),男,博士研究生,主研方向为智能决策、先进航空火力控制;周德云,教授、博士;杨振、潘潜,博士研究生。
  • 基金资助:
    国家自然科学基金(61603299,61602385);中央高校基本科研业务费专项资金(3102019ZX016)。

Fast Decision Making Algorithm for Weapon Target Assignment Based on Adaptive Resonance Theory

ZHANG Kai, ZHOU Deyun, YANG Zhen, PAN Qian   

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
  • Received:2019-07-22 Revised:2019-09-28 Published:2019-10-10

摘要: 针对武器目标分配(WTA)的求解实时性问题,建立基于火力集合划分的WTA数学模型,并提出一种基于模糊自适应谐振理论的邻域搜索(FART-NS)快速决策算法。利用模糊自适应谐振理论的快速泛化能力提高算法实时性,引入虚拟节点提升邻域搜索算法在WTA解空间的寻优能力,形成快速泛化-邻域优化-在线学习的闭环机制,使FART-NS算法对训练集精度和采样密度具有较强的鲁棒性。仿真结果表明,该算法在时间复杂度上优于BBA、改进GA等主流算法,能较好平衡WTA问题的求解实时性和收敛性。

关键词: 武器目标分配, 决策支持, 自适应谐振理论, 邻域搜索, 机器学习

Abstract: In order to solve the real-time problem of Weapon Target Assignment(WTA),this paper establishes a mathematical model of WTA based on the division of fire set,and proposes a fast decision making algorithm of Neighborhood Search based on Fuzzy Adaptive Resonance Theory(FART-NS).The fast generalization ability of Fuzzy Adaptive Resonance Theory(FART) is used to improve the real-time performance of the algorithm.The virtual node is introduced to improve the optimization ability of Neighborhood Search(NS) algorithm in WTA solution space.A closed-loop mechanism of fast generalization neighborhood optimization online learning is formed,which makes the FART-NS algorithm robust to training set accuracy and sampling density.Simulation results show that the FART-NS algorithm is better than the mainstream algorithms such as BBA and improved GA in time complexity,and it can balance the real-time performance and convergence of WTA problem.

Key words: Weapon Target Assignment(WTA), decision making support, Adaptive Resonance Theory(ART), Neighborhood Search(NS), machine learning

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