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

中图分类号: 

• TP391