摘要
针对无人机空地作战效能评估问题,提出了基于自适应粒子群算法(APSO)优化反向传播神经网络(BPNN)的无人机空地作战效能评估方法。首先介绍了APSO算法和BP神经网络的基本原理和算法流程;然后对无人机空地作战效能评估的主要影响因素进行分析,归纳总结了无人机空地作战效能评估的指标体系,接着构建了APSO-BP神经网络评估模型;最后对评估模型进行了仿真验证。仿真结果表明,该模型可以准确有效地对无人机空地作战效能进行评估。
Considering the problem of effectiveness evaluation,this paper presents an effectiveness evaluation model for UAV air-to-ground attack which is based on APSO-BP neural network. Firstly,the basic principles of APSO algorithm and BP neural network are introduced respectively. Secondly,the main influencing factors of effectiveness evaluation of air-to-ground attack are analyzed and summarized,and then APSO-BP neural network is constructed. Finally,simulation experiments are performed using historical statistics and the simulation results show that the proposed method can reduce the training error and improve the evaluation accuracy,which implies that the proposed algorithm is a better method to solve the problem of effectiveness evaluation of UAV air-to-ground attack.
出处
《飞行力学》
CSCD
北大核心
2018年第1期88-92,共5页
Flight Dynamics
基金
国家自然科学基金资助(61503255)
沈阳市科技创新专项资金资助(14042200
F14231129)
关键词
无人机
空地作战
效能评估
UAV
air-to-ground attack
effectiveness evaluation
