摘要
作为民航运输的两翼之一,通用航空安全水平直接影响民机系统的安全。目前,对通航以及鸟击事故征候进行预测的研究较少,本文根据收集到的美国从事通用航空活动发生鸟击事故征候安全状况数据,采用长短期记忆(long short-term memory,LSTM)神经网络模型对鸟击事故征候数据进行训练和预测。实验结果显示,与传统模型相比,LSTM模型具有更好的预测效果,精确度更高。基于此,提出了预测稳定性更好的LSMT-均方根误差(LSTM-root mean square error,LSTM-R)模型,为通用航空鸟击事故征候预测提供了手段和方法,加强了通用航空安全管理。
As one of the two wings of civil aviation transportation,the general aviation safety directly affects the safety of the civil aircraft systems.There are very few research on bird strike symptom prediction.According to the general aviation safety situation of the bird strike symptom in the United States,the long short-term memory(LSTM)neural network model is used to train and predict the bird strike symptom data.The experimental results show that compared with the traditional models,the LSTM model has a better fitting effect and a higher accuracy.Based on this station,an LSTM-root mean square error(LSTM-R)model with a better prediction stability is proposed,which provides a means and method for the general aviation bird strike symptom prediction,and strengthens the safety management of the general aviation.
作者
熊明兰
王华伟
徐怡
付强
XIONG Minglan;WANG Huawei;XU Yi;FU Qiang(College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2020年第9期2033-2040,共8页
Systems Engineering and Electronics
基金
国家自然科学基金联合基金(U1833110)资助课题。
关键词
通用航空安全
鸟击事故征候
事故征候预测
长短期记忆
general aviation safety
bird strike symptom
symptom prediction
long short-term memory(LSTM)
