基于粗糙集和神经网络的无人机侦察效能评估  

UAV Reconnaissance Effectiveness Evaluation Based on Rough Set and Neural Network

作　　者：何媛[1] 甘旭升[2] 涂从良 孟祥伟[2] HE Yuan;GAN Xu-sheng;TU Cong-liang;MENG Xiang-wei(XiJing University,Xi’an 710123,China;Air Traffic Control and Navigation College,Air Force Engineering University,Xi’an 710051,China)

机构地区：[1]西京学院,西安7101231 [2]空军工程大学空管领航学院,西安710051

出　　处：《火力与指挥控制》2021年第3期20-25,共6页Fire Control & Command Control

基　　金：国家自然科学基金资助项目(11726624)。

摘　　要：针对无人机在多条件下的侦察效能评估问题,提出一种基于粗糙集和神经网络的无人机侦察效能评估方法。在该方法中,寻找影响因素构建无人机侦察效能评估指标体系;结合粗糙集理论去除当中的冗余因素;并在处理因素基础上利用遗传优化的BP神经网络构建无人机侦察效能的评估模型,以提高预测精度。仿真结果表明:该模型不仅能够克服传统BP神经网络容错性差,收敛速度慢的缺点,而且可以较好地完成无人机侦察效能评估。For the evaluation of UAV reconnaissance effectiveness under multiple conditions,an UAV reconnaissance effectiveness evaluation method based on rough set and neural network is proposed.In the method,the influencing factors are determined to construct the UAV reconnaissance effectiveness index system,then the redundant factors are removed combined with rough set theory,finally on the basis of the simplified factors BP neural network optimized through genetic algorithm is used to build an evaluation model of UAV reconnaissance effectiveness for improving the prediction accuracy.The simulation result shows that the method can not only overcome the shortcomings of the traditional BP neural network,such as poor fault tolerance and slow convergence speed,but also better evaluate the UAV reconnaissance effectiveness.

关 键 词：无人机 效能评估 粗糙集 BP神经网络 

分 类 号：TJ01[兵器科学与技术—兵器发射理论与技术]