General aviation safety research based on prediction of bird strike symptom.

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. [ABSTRACT FROM AUTHOR]