Projectile impact point prediction method based on GRNN  

基于广义回归神经网络的弹丸落点预报方法（英文）

作　　者：黄鑫[1] 赵捍东[1] 

机构地区：[1]中北大学,机电工程学院,山西太原030051

出　　处：《Journal of Measurement Science and Instrumentation》2016年第1期7-12,2,共6页测试科学与仪器（英文版）

基　　金：Project Funded by Chongqing Changjiang Electrical Appliances Industries Group Co.,Ltd

摘　　要：In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.

关 键 词：trajectory correction impact point prediction generalized regression neural network（GRNN） numerical integra-tion method 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]