中国舰船研究2025，Vol.20Issue(1)：76-84,9.DOI:10.19693/j.issn.1673-3185.03740
基于增强Bi-LSTM的船舶运动模型辨识
Ship motion identification model based on enhanced Bi-LSTM
摘要
Abstract
[Objective]Aiming 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.[Methods]First,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 predic-tion 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.[Results]The 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 0.999 13,demonstrating prediction accuracy significantly higher than that of the SVM,GRU,and LSTM models.[Conclusion]The proposed enhanced Bi-model has excellent generalization performance and excel-lent prediction stability and precision,and effectively realizes ship motion identification.关键词
系统辨识/非参数化建模/一维卷积神经网络/双向长短期记忆神经网络/多头自注意力机制Key words
identification(control systems)/non-parametric modelling/one-dimensional convolutional neural network(1D-CNN)/bi-directional long short-term memory(Bi-LSTM)neural network/multi-head self-attention mechanism分类
交通工程引用本文复制引用
张浩晢,杨智博,焦绪国,吕成兴,雷鹏..基于增强Bi-LSTM的船舶运动模型辨识[J].中国舰船研究,2025,20(1):76-84,9.基金项目
国家自然科学基金资助项目(62373209,61803220,61573203,62203249) （62373209,61803220,61573203,62203249）
山东省重点研发计划(重大科技创新工程)资助项目(2022CXGC010608) （重大科技创新工程）
