State Change Trend Prediction of Aircraft Pump Source System Based on GRU Network

摘要

Aiming at the problem that the traditional trend prediction method has a low accuracy in predicting the state change of the pump source system due to the high working intensity and bad working environment of the aircraft pump source system, this paper designs a GRU network prediction model and studies the state change trend of the aircraft pump source system. Firstly, the acquired data of aircraft pump source system are preprocessed, and the processed data are reconstructed in phase space to construct the training sample set of network parameters. The network is trained with training samples, and the optimal trend prediction model is created by constantly adjusting the number of hidden layer neurons. The optimal trend prediction model is used to obtain the trend prediction results. In order to demonstrate the effectiveness of the GRU trend prediction model, several other prediction methods have been compared with the GRU in this paper. The results show that GRU trend prediction model has higher prediction accuracy than ARMA model and traditional BP network. Compared with the LSTM prediction model which has similar prediction performance, GRU prediction model has more advantages in model training speed and better engineering application value.