舰船电子工程2025，Vol.45Issue(1)：166-169,196,5.DOI:10.3969/j.issn.1672-9730.2025.01.033
基于元学习的船舶操纵性预报模型研究
Research on Ship Maneuverability Forecast Model Based on Meta-learning
摘要
Abstract
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.关键词
船舶操纵性/元学习/预报模型/机器学习Key words
ship maneuverability/meta-learning/prediction model/machine learning分类
交通工程引用本文复制引用
张泽瑞,董宜滔,陈伟民,吴梓鑫,徐延军..基于元学习的船舶操纵性预报模型研究[J].舰船电子工程,2025,45(1):166-169,196,5.基金项目
国家重点研发计划(编号:2022YFB4300802)资助. （编号:2022YFB4300802）
