上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 2: 156 - 165

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.316

船舶海洋与建筑工程

基于人工神经网络的极地船舶冰阻力预报方法

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  • 1.江苏科技大学 船舶与海洋工程学院,江苏 镇江 212100
    2.上海交通大学 船舶海洋与建筑工程学院,上海 200240
孙乾洋 (1995-),博士生,主要从事冰载荷方面的研究.

收稿日期: 2022-08-19

  修回日期: 2023-02-17

  录用日期: 2023-02-20

  网络出版日期: 2024-03-04

基金资助

国家重点研发计划(2022YFE0107000);国家自然科学基金面上项目(52171259);工信部高技术船舶科研项目(工信部重装函[2021]342号)

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An Artificial Neural Network-Based Method for Prediction of Ice Resistance of Polar Ships

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  • 1. School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, Jiangsu, China
    2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2022-08-19

  Revised date: 2023-02-17

  Accepted date: 2023-02-20

  Online published: 2024-03-04

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摘要

极地船舶冰区航行时,冰阻力的准确预报在保障船舶航行安全方面起着重要作用.近年来,机器学习在船舶方面的应用越来越广泛,其中,人工神经网络(ANN)是机器学习领域中一种常用的方法.本文的重点是设计一个用于预报极地船舶冰阻力的ANN模型.参考传统的经验和半经验公式,选择合适的输入特征参数,通过大量的船舶模型试验数据来训练神经网络,搭建径向基(RBF)神经网络模型,并选用遗传算法(GA)进行模型优化.研究表明,基于7个特征参数输入的遗传算法优化径向基(RBF-GA)神经网络模型具有良好的泛化效果,与模型试验和实船试验数据对比,平均误差在8%左右,具有较高的精度,可作为冰阻力预报工具.

关键词： 冰阻力; 机器学习; 径向基函数神经网络; 遗传算法; 船舶试验

本文引用格式

孙乾洋, 周利, 丁仕风, 刘仁伟, 丁一 . 基于人工神经网络的极地船舶冰阻力预报方法[J]. 上海交通大学学报, 2024 , 58(2) : 156 -165 . DOI: 10.16183/j.cnki.jsjtu.2022.316

Abstract

Accurate prediction of ice resistance plays an important role in ensuring the safety of ship sailing in polar navigation in ice areas. In recent years, machine learning has been widely used in the field of ships, among which artificial neural network (ANN) is a common method. The focus of this paper is to design an ANN model for predicting the ice resistance of polar ships. According to the traditional empirical and semi-empirical formula, appropriate input characteristic parameters are selected. The radial basis function (RBF) neural network model is built based on a large number of ship model test data, and the genetic algorithm (GA) is used to optimize the model. The research shows that the radial basis function neural network model optimized by genetic algorithm (RBF-GA) based on seven characteristic parameters input has good generalization effect. Compared with the model test and full-scale test data, the average error is about 8%, which shows that the RBF-GA model has a high accuracy, and can be used as a tool for ice resistance prediction.

Key words： ice resistance; machine learning; radial basis function (RBF) neural network; genetic algorithm (GA); ship test

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