基于改进LSTM模型的航空安全预测方法研究  

Aviation safety prediction method research based on improved LSTM model

作　　者：曾航 张红梅 任博 崔利杰[1] 武江南 ZENG Hang;ZHANG Hongmei;REN Bo;CUI Lijie;WU Jiangnan(Equipment Management and Unmanned Aerial Vehicle Engineering College, Air Force Engineering University, Xi’an 710051, China;Science and Technology on Electro-optic Control Laboratory, Luoyang 471000, China)

机构地区：[1]空军工程大学装备管理与无人机工程学院,陕西西安710051 [2]光电控制技术重点实验室,河南洛阳471000

出　　处：《系统工程与电子技术》2022年第2期569-576,共8页Systems Engineering and Electronics

基　　金：国家自然科学基金(71701210);陕西省自然科学基金(2019JQ-710)资助课题。

摘　　要：精确的航空安全预测是科学开展安全预警的前提。航空事故不仅致因机理复杂,还存在迟滞效应,给安全样本时序信息的深度挖掘加大了难度。基于此,提出一种基于改进长短期记忆(long short-term memory,LSTM)模型的航空安全预测新方法。首先基于相关系数热图优选致因指标,再以步进搜索和Adam算法相结合的方式优化LSTM模型超参数,最后以2019年某型运输机事故数据为算例,选取多种常用时序预测模型作为对照。实验结果表明本文所提方法,预测误差较现有方法降低了28%以上,同时具有较好的泛化能力和鲁棒性。Accurate aviation safety prediction is the premise of efficient safety early warning.Aviation accidents are not only caused by complex mechanism,but also have hysteresis effect,which makes it more difficult to excavate the time-series information of safety samples in depth.Based on this,an aviation safety forecast method based on improved long short-term memory(LSTM)model is proposed.Firstly,cause indicators are optimized based on the correlation coefficient heat maps.Then the model parameters of LSTM model are optimized by the step search and Adam algorithm.Finally,taking the accident data of a certain transport aircraft in 2019 as an example,select a variety of time-series forecasting model commonly used as a control.According to the experimental results,compared with the existing methods,the prediction error of the proposed method can be reduced by more than 28%,with excellent generalization ability and robustness.

关 键 词：航空安全 神经网络 长短期记忆 堆叠式 多步预测 

分 类 号：V328[航空宇航科学与技术—人机与环境工程]