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.

引用

页码：156 / 165

页数：9

共 41 条

[1]

ZHOU L, SU B, RISKA K, Et al., Numerical simulation of moored structure station keeping in level ice, Cold Regions Science and Technology, 71, pp. 54-66, (2012)

[2]

HU J, ZHOU L., Further study on level ice resistance and channel resistance for an icebreaking vessel, International Journal of Naval Architecture and Ocean Engineering, 8, pp. 169-176, (2016)

[3]

MARKOPOULOS A P, GEORGIOPOULOS S, MANOLAKOS D E., On the use of back propagation and radial basis function neural networks in surface roughness prediction, Journal of Industrial Engineering International, 12, pp. 389-400, (2016)

[4]

KOUSHAN K., Empirical prediction of ship resistance and wetted surface area using artificial neural networks, Practical Design of Ships & Other Floating Structures, 1, pp. 501-507, (2001)

[5]

COUSER P, MASON A, MASON G, Et al., Artificial neural networks for hull resistance prediction, 3 rd International Conference on Computer and IT Applications in the Maritime Industries, pp. 391-402, (2004)

[6]

CUI H, TURAN O, SAYER P., Learning-based ship design optimization approach, Computer-Aided Design, 44, pp. 186-195, (2012)

[7]

YANG Y, TU H, SONG L, Research on accurate prediction of the container ship resistance by RBFNN and other machine learning algorithms [J], Journal of Marine Science and Engineering, 9, 4, pp. 376-393, (2021)

[8]

CHEN Aiguo, YE Jiawei, Numerical experimental study of ship resistance calculation based on neural network, Shipbuilding of China, 51, 2, pp. 21-27, (2010)

[9]

ZHANG Qiaoyu, HUANG Guofu, JIN Jianhai, Research on ship resistance prediction model based on machine learning, Ship Science and Technology, 41, 23, pp. 6-10, (2019)

[10]

KIM J H, KIM Y, LU W., Prediction of ice resistance for ice-going ships in level ice using artificial neural network technique, Ocean Engineering, 9, 6, pp. 613-623, (2020)

← 1 2 3 4 5 →