摘要
[目的]针对基于数据驱动的船舶建模策略获得的模型预测精度低、适应性差等特点,提出一种增强的双向长短期记忆(Bi-LSTM)神经网络用于船舶的高精度非参数化建模。[方法]首先,利用Bi-LSTM神经网络的特点,实现对序列双向时间维度的特征提取。基于此,设计一维卷积神经网络(1D-CNN)提取序列的空间维度特征。然后,采用多头自注意力机制(MHSA)多角度对序列进行自适应加权处理。利用KVLCC2船舶航行数据,将所提增强Bi-LSTM模型与支持向量机(SVM)、门控循环单元(GRU)、长短期记忆神经网络(LSTM)模型的预测效果进行对比。[结果]所提增强Bi-LSTM模型在测试集中均方根误差(RMSE)、平均绝对误差(MAE)性能指标分别低于0.015和0.011,决定系数(R2)高于0.99913,预测精度显著高于SVM,GRU,LSTM模型。[结论]增强Bi-LSTM模型泛化性能优异,预测稳定性及预测精度高,有效实现了船舶的运动模型辨识。
[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 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.[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 0.99913,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 excellent prediction stability and precision,and effectively realizes ship motion identification.
作者
张浩晢
杨智博
焦绪国
吕成兴
雷鹏
ZHANG Haozhe;YANG Zhibo;JIAO Xuguo;LÜChengxing;LEI Peng(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China)
出处
《中国舰船研究》
北大核心
2025年第1期76-84,共9页
Chinese Journal of Ship Research
基金
国家自然科学基金资助项目(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)。
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
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
