基于改进LSTM模型的航空安全预测方法研究

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...

Aviation safety prediction method research based on improved LSTM model

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.