面向射击密集度的随机因素影响分析  

Influence of the Random Factors on Firing Dispersion

作　　者：王丽群[1] 杨国来[1] 葛建立[1] 

机构地区：[1]南京理工大学机械工程学院,江苏南京210094

出　　处：《火炮发射与控制学报》2016年第4期54-57,共4页Journal of Gun Launch & Control

基　　金：国家"973"专题(6132490303);国家重大科学仪器设备开发专项(2013YQ470765)

摘　　要：射击密集度是火炮关键战术技术指标,是弹、炮、药、气象环境等各种随机因素综合作用的结果。为了分析弹丸起始扰动、初速、弹丸结构参数、风速等随机因素对射击密集度的影响,采用RBF神经网络拟合射击密集度计算程序非线性,较好地解决了直接灵敏度分析中回归模型不可靠问题。基于最优拉丁超立方试验设计进行了射击密集度灵敏度计算,通过回归分析与方差分析获得影响射击密集度的主要因素,以及各随机因素对射击密集度的影响规律。该研究为随机因素优化奠定了基础,可为火炮总体设计提供理论参考。Firing dispersion,which is affected comprehensively by all kinds of random factors such as projectiles,artillery,ammunition and meteorological environment,is a crucial technical indicator of artillery. In order to analyze the influence of random factors including the projectile initial disturbance and velocity,the projectile structure parameters and wind speed,a RBF neural network was established to simulate the nonlinearity of the firing dispersion calculation program. It solved the problem of the non-reliability of the regression model in direct sensitivity analysis. The firing dispersion sensitivity was calculated based on the optimal Latin hypercube experimental design. The main random factors which affect the firing dispersion as well as the influential rule of different random factors to fire density were obtained through the regression analysis and the variance analysis. The research lays a foundation for the random factors optimization and provides theoretical reference for general design of artillery.

关 键 词：射击密集度 随机因素 神经网络 最优拉丁超立方试验设计 灵敏度分析 

分 类 号：TJ301[兵器科学与技术—火炮、自动武器与弹药工程]