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

被引:0
作者
Sun Q. [1 ]
Zhou L. [2 ]
Ding S. [1 ]
Liu R. [1 ]
Ding Y. [1 ]
机构
[1] School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Jiangsu, Zhenjiang
[2] School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai
来源
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2024年 / 58卷 / 02期
关键词
genetic algorithm (GA); ice resistance; machine learning; radial basis function (RBF) neural network; ship test;
D O I
10.16183/j.cnki.jsjtu.2022.316
中图分类号
学科分类号
摘要
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. © 2024 Shanghai Jiaotong University. All rights reserved.
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页码:156 / 165
页数:9
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