哈尔滨工程大学学报2026，Vol.47Issue(3)：595-603,9.DOI:10.11990/jheu.202411042
基于深度学习的舰船运动参数长时预测方法
Long-term prediction method of ship motion parameters based on deep learning
摘要
Abstract
To address the challenge of accurately predicting long-term ship motion due to its nonlinear and stochas-tic characteristics,this study investigates prediction methods for multi-dimensional ship motion parameters using various deep learning models.A dataset of six-degree-of-freedom(6-DOF)motion parameters was first constructed by collecting sensor data from a 20 t vessel.Subsequently,five deep learning models LSTM,Dlinear,PatchTST,FPT,and AutoTimes were employed to develop long-term prediction models for ship roll,pitch,and heave.The performance of these models was evaluated using mean squared error(MSE)and mean absolute error(MAE).The results demonstrate that among the five models,the one based on the AutoTimes method achieved the best perfor-mance,accounting for 61.11%of the minimum error indices.Further analysis revealed that increasing the length of the historical look-back window(which has an optimal value)and embedding textual timestamp tokens can both effectively enhance the model's prediction accuracy.The findings of this study offer a valuable reference for the se-lection and optimization of high-precision models for long-term prediction of ship motion.关键词
舰船多维运动/长时预测/深度学习/大语言模型/横摇/纵摇/垂荡/均方误差/平均绝对误差Key words
ship multi-dimension/long-term prediction/deep learning/large language models/rolling/pitching/heaving/mean squared error/mean absolute error分类
交通工程引用本文复制引用
李莹,白鹏英,李健,王资洋,张誉译,张晓今,陈伟..基于深度学习的舰船运动参数长时预测方法[J].哈尔滨工程大学学报,2026,47(3):595-603,9.基金项目
卓越青年基金项目(2023-JCJQ-ZQ-036). （2023-JCJQ-ZQ-036）
