Research on intelligent forecasts of fl ight actions based on the implemented bi-LSTM

Rapid identi fi cation of fl ight actions by utilizing fl ight data is more realistic so the quality of fl ight training can be objectively assessed. The bidirectional long shortterm memory (bi-LSTM) algorithm is implemented to forecast the fl ight actions of aircraft. The dataset containing the fl ight actions is structured by collecting tagged fl ight data when real fl ight training is exercised. However, the dataset needs to be preprocessed and annotated with expert rules. One of the deep learning (DL) methods, called the bi-LSTM algorithm, is implemented to train and test, and the pivotal parameters of the algorithm are optimized. Finally, the constructed model is applied to forecast the fl ight actions of aircraft. The training ' s accuracy and loss rates are computed. The duration is kept between 1 through 3 h per session. Thus, the development of training the model is continued until an accuracy rate above 85% is achieved. The word -run inference time is kept under 2 s. Finally, the proposed algorithm ' s speci fi c characteristics, which are short training time and high recognition accuracy, are achieved when complex rules and large sample sizes exist.