摘要
论文基于元学习提出了一种快速、精确的机器学习方法,通过对自由自航模操纵性试验数据分析与建模获得预报模型,进而用模型预报船舶操纵性能目标参数,最终对船舶设计工作起到先验作用。相较于依赖大数据集的传统机器学习方法,元学习可以在有限数据量的基础上建立精度较高且泛化性强的数据模型。首先,该研究利用基于模型的特征选择算法为各目标参数筛选与其相关性强的特征参数,然后对各目标参数建立不同的预报任务,最后利用元学习方法对各预报任务进行学习获得最优的预报模型。经测试,该文的元学习模型预报精度较高,12个目标参数预报任务的平均模型损失稳定在0.65,对于船舶设计前期操纵性能评估具有显著的工程意义。
Based on meta-learning,this paper proposes a fast and accurate machine learning method,which obtains the forecast model with the free self-propelled model maneuverability experiment data.Using this model to forecast ship maneuvering performance's target parameters,it plays a priori role in ship design.Meta-learning is different from traditional machine learning methods that rely on large data sets,it can establish model with limited amount of data.Firstly,the model-based feature selection algorithm is used to screen strong correlation feature parameters for each target parameter.Secondly,different forecast tasks are established for each target parameter.Finally,the meta-learning method is used to learn each forecast task to obtain the optimal forecast model.After testing,the meta-learning model predicted accurately.The average model loss of 12 forecast tasks is stably at 0.65.This reasearch about the ship maneuvering forecast model has important engineering significance for manoeuvrability performance evaluation in the early ship designing stage.
作者
张泽瑞
董宜滔
陈伟民
吴梓鑫
徐延军
ZHANG Zerui;DONG Yitao;CHEN Weimin;WU Zixin;XU Yanjun(Shanghai Ship and Shipping Research Institute Co.,Ltd.,Shanghai 200135;State Key Laboratory of Navigation and Safety Technology,Shanghai 200135;Key Laboratory of Marine Technology Ministry of Communications,Shanghai 200135;COSCO Shipping Technology Co.,Ltd.,Shanghai 200135)
出处
《舰船电子工程》
2025年第1期166-169,196,共5页
Ship Electronic Engineering
基金
国家重点研发计划(编号:2022YFB4300802)资助。
关键词
船舶操纵性
元学习
预报模型
机器学习
ship maneuverability
meta-learning
prediction model
machine learning
作者简介
张泽瑞,男,硕士研究生,研究方向:船舶水动力数值计算;董宜滔,男,硕士研究生,研究方向:智能交通系统its、计算机视觉;陈伟民,男,研究员,研究方向:船舶水动力研究及数值计算;吴梓鑫,男,副研究员,研究方向:船舶操纵性试验及研究;徐延军,男,正高级工程师,研究方向:智能交通系统its、交通信息化。
