中国舰船研究2024，Vol.19Issue(6)：303-311,9.DOI:10.19693/j.issn.1673-3185.03755
基于贝叶斯优化的时间卷积网络船舶航迹预测
Ship track prediction based on Bayesian optimization in temporal convolutional networks
摘要
Abstract
[Objective]As the traditional ship trajectory prediction method is prone to gradient explosion and long calculation time,this paper seeks to improve its accuracy and calculation efficiency by proposing a ship trajectory prediction model based on an improved Bayesian optimization algorithm(IBOA)and temporal convolution network(TCN).[Method]A temporal pattern attention(TPA)mechanism is introduced to ex-tract the weights of each input feature and ensure the timing of the historical flight track data.At the same time,a reversible residual network(RevNet)is introduced to reduce the memory occupied by TCN model training.The IBOA is then used to find the optimality of the hyperparameters in the TCN(size of kernel K,ex-pansion coefficient d).The model is finally validated using a five-fold cross-validation method,and trajectory prediction is carried out after obtaining the optimal model.[Result]The trajectory data is collected by auto-matic identification system(AIS)and verified.The root mean square error(RMSE)is found to be increased by 5.5×10-5,3.5×10-4 and 6×10-4 in weak coupling,medium coupling and strong coupling track prediction respec-tively.[Conclusion]The proposed network has good adaptability to complex trajectories and higher accur-acy than the traditional model and long short-term memory(LSTM)model,while maintaining high prediction accuracy for trajectories with strong coupling.关键词
导航/神经网络/贝叶斯优化算法/时间卷积网络/时间模式注意力机制模块/可逆残差网络/AIS数据Key words
navigation/neural networks/Bayesian optimization algorithm/temporal convolutional net-works/temporal pattern attention mechanism module/reversible residual networks/AIS data分类
交通工程引用本文复制引用
李金源,朱发新,滕宪斌,毕齐林..基于贝叶斯优化的时间卷积网络船舶航迹预测[J].中国舰船研究,2024,19(6):303-311,9.基金项目
浙江省大学生科技创新活动计划资助项目(2023R411044) （2023R411044）
