A deep learning combined prediction model for prediction of ship motion attitude in real conditions

Abstract

ABSTRACT This paper presents the TRSA-EMD-GAN ship motion attitude prediction model, which utilizes self-attention and generative adversarial networks (GAN) to accurately predict ship motion attitudes. The TRSA mechanism based on time residuals is incorporated into the model to capture the different influences of various attitude points on the prediction and their temporal relationships by using a time mask. Moreover, the model employs a variation mode decomposition generative adversarial network (VMD-GAN) for ship motion attitude prediction through feature fusion. In the VMD-GAN model, VMD is combined with a GRU neural network as the generator, while a convolutional neural network serves as the discriminator. Simulation experiments confirm the effectiveness of the TRSA-VMD-GAN model in predicting ship motion attitudes, resulting in reduced prediction errors and improved accuracy and efficiency.