智能系统学报2025，Vol.20Issue(3)：571-583,13.DOI:10.11992/tis.202408004
基于自优化神经网络的船舶运动模型辨识
Identification of ship motion model based on self-optimizing neural network
摘要
Abstract
An accurate ship motion model stands as the cornerstone of autonomous ship systems.To enhance the preci-sion of ship motion modeling,an improved snow ablation optimizer(ISAO)is first introduced.Subsequently,a network model,BITCA,which integrates a bidirectional temporal convolutional network(Bi-TCN)with the attention mechan-ism(AM),is proposed.Furthermore,by combining the ISAO with BITCA,a hybrid identification model for ship mo-tion,termed ISAO-BITCA,is established.This model initially leverages the Bi-TCN to deeply explore the hidden fea-tures of ship motion sequences across both temporal and spatial dimensions,while introducing the AM to mitigate in-formation loss.Utilizing the ISAO,the hyperparameter combination for the BITCA model is autonomously searched and optimized.Simulation results demonstrate that the BITCA model optimized by the ISAO achieves reductions in the root mean square error for ship heading angle,yaw rate,roll angle,and total speed predictions by 54.1％,28.21％,5.88％,and 40％,respectively,providing an effective means for the accurate identification of ship motion models.关键词
船舶运动建模/改进雪融优化器/双向时间卷积网络/注意力机制/优化/超参数/预测/辨识Key words
ship motion modeling/improved snow ablation optimizer/bidirectional temporal convolutional network/at-tention mechanism/optimize/hyperparameter/predict/identification分类
信息技术与安全科学引用本文复制引用
张浩晢,杨智博,焦绪国,吕成兴,朱齐丹..基于自优化神经网络的船舶运动模型辨识[J].智能系统学报,2025,20(3):571-583,13.基金项目
国家自然科学基金项目(62203249,61803220) （62203249,61803220）
山东省重大创新工程项目(2022CXGC010608) （2022CXGC010608）
山东省自然科学基金项目(ZR2021QF115). （ZR2021QF115）
