基于 PSO-BiLSTM 神经网络的机身筒段应力预测

Before aircraft fuselage components are assembled and docked，shape control tools are usually used to adjust the shape of the fuselage tube section to meet the docking standard. In order to avoid damage to the fuselage due to excessive local stress during the shape control process，it is necessary to monitor changes in stress. This re⁃ search proposes a stress prediction method，which combines Particle Swarm Optimization and Bidirectional Long Short-Term Memory（PSO-BiLSTM）neural network. In this paper，the model input data is converted into sliding win⁃ dow sequential data with extremely high data correlation，and the stress data set is trained and tested. The experi⁃ mental results show that the PSO-BiLSTM neural network has obvious advantages in processing sequential data. This is because the PSO-BiLSTM network has long memory cells and high model capacity. Stress prediction loss con⁃ verges within 0. 3% Root Mean Square Error（RMSE）error range. Compared with the competitive model RNN net⁃ work，the standard Long Short-Term Memory（LSTM）network and the Bidirectional Long Short-Term Memory（BiL⁃ STM）neural network，the PSO-BiLSTM model not only predicts more accurately，but also significantly improves train⁃ ing efficiency.