Ship motion identification model based on enhanced Bi-LSTM - Haozhe ZHANG; Zhibo YANG; Xuguo JIAO; Chengxing LÜ; Peng LEI (Zhongguo Jianchuan Yanjiu)
ObjectiveAiming at the low prediction precision and poor adaptability of ship models based on the data-driven modeling strategy, an enhanced bi-directional long short-term memory (Bi-LSTM) model is proposed for the high-precision non-parametric modeling of ships. MethodsFirst, the feature extraction of the bi-directional time dimension is realized using bi-directional long short-term memory (Bi-LSTM) neural networks. On this basis, the spatial dimension features of the one-dimensional convolutional neural network （1D-CNN） extraction sequence are designed. Then, a multi-head self-attention (MHSA) mechanism is used to deal with the sequence from multiple angles. Finally, using the navigation data of KLVCC2 ships, the prediction effects of the enhanced Bi-LSTM model are compared with those of the Support Vector Machine (SVM), Gate Recurrent Unit (GRU), and long short-term memory (LSTM) models.ResultsThe Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) performance indicators of the enhanced Bi-LSTM model in the test set are lower than 0.015 and 0.011 respectively, and the coefficient of determination（R2）is higher than 0.99913, demonstrating prediction accuracy significantly higher than that of the SVM, GRU, and LSTM models.ConclusionThe proposed enhanced Bi-model has excellent generalization performance and excellent prediction stability and precision, and effectively realizes ship motion identification.