【摘要】 高空风场对制导炮弹弹道诸元有较大影响，为了提高控制精度，如何快速准确获取高空风场数据从而修正气象参数对弹道特性的影响是当前研究的热点。本文中创新性的使用北方苍鹰优化算法(NGO)优化反向传播(BP)神经网络的原始权值，对高空风场参数按照高度进行辨识研究。通过采集的高空风场参数和基于实验的弹道数据进行仿真分析，使用误差统计学评估法和仿真弹道数据结果分析两种方法对辨析出的高空风场参数精度分别进行评估，分析结果表明：利用NGO-BP神经网络法相对于反向传播神经网络法对高空风场参数辨识精度上有明显提高。更多还原

【Abstract】 The high-altitude wind field has a significant impact on the ballistic characteristics of guided shells.In order to improve control accuracy, how to quickly and accurately obtain high-altitude wind field data and correct the impact of meteorological parameters on ballistic characteristics is currently a hot research topic.This article innovatively uses the Northern Goshawk Optimization Algorithm(NGO) to optimize the original weights of the Backpropagation(BP) neural network, and conducts identification research on high-altitude wind field parameters according to height.By collecting high-altitude wind field parameters and conducting simulation analysis based on experimental trajectory data, the accuracy of the identified high-altitude wind field parameters was evaluated using two methods: error statistics evaluation method and simulation trajectory data result analysis.The analysis results showed that the accuracy of identifying high-altitude wind field parameters using the NGO BP neural network method was significantly improved compared to the backpropagation neural network method.更多还原