Online Data-Driven Integrated Prediction Model for Ship Motion Based on Data Augmentation and Filtering Decomposition and Time-Varying Neural Network

Online prediction for ship motion with strong nonlinear characteristics under harsh sea states will significantly reduce the damage of large accidents. Therefore, an integrated ship motion online prediction model consisting of a data augmentation algorithm based on the Improved Temporal Convolutional Network and Time Generative Adversarial Network (ITCN-TGAN), and an Improved Empirical Mode Decomposition (IEMD) and a Time-Varying Neural Network based on Global Time Pattern Attention (GTPA-TNN), is proposed in this article. The results of the validation tests in which the container ship KCS is taken as the example show that the synthetic data generated by ITCN-TGAN based on the dataset with few nonlinear samples are very similar to the original data, which proves that the synthetic data have high authenticity and can be used as training data to reduce the sampling cost; the input signal is decomposed into multiple Intrinsic Mode Functions (IMFs) by IEMD without noise diffusion, an endpoint effect, or mode mixing occurring in it, which indirectly improved the accuracy; and the dynamic sliding window adaptively adjusts the input sequence length according to the waveform characteristics to improve the computational stability of the model, the accuracy of GTPA-TNN can maintain a high level during the prediction period in various working conditions, and the error distribution is almost the same, which suggests that the integrated model has strong robustness and can realize the goal of online prediction of ship motion under harsh sea conditions.