基于自适应谐振理论的武器目标分配快速决策算法  

Fast Decision Making Algorithm for Weapon Target AssignmentBased on Adaptive Resonance Theory

作　　者：张凯[1] 周德云[1] 杨振[1] 潘潜[1] ZHANG Kai;ZHOU Deyun;YANG Zhen;PAN Qian(School of Electronics and Information,Northwestern Polytechnical University,Xi’an 710072,China)

机构地区：[1]西北工业大学电子信息学院,西安710072

出　　处：《计算机工程》2020年第9期283-291,297,共10页Computer Engineering

基　　金：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)。

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。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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]