文章基本信息

标题：Research on Accurate Prediction of the Container Ship Resistance by RBFNN and Other Machine Learning Algorithms
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作者：Yunfei Yang ; Haiwen Tu ; Lei Song 等
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2021
卷号：9
期号：4
页码：376
DOI：10.3390/jmse9040376
语种：English
出版社：MDPI AG
摘要：Resistance is one of the important performance indicators of ships. In this paper, a prediction method based on the Radial Basis Function neural network (RBFNN) is proposed to predict the resistance of a 13500 transmission extension unit (13500TEU) container ship at different drafts. The predicted draft state in the known range is called interpolation prediction; otherwise, it is extrapolation prediction. First, ship features are extracted to make the resistance Rt prediction. The resistance prediction results show that the performance of the RBFNN is significantly better than the other four machine learning models, backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Then, the ship data is processed in a dimensionless manner, and the models mentioned above are used to predict the total resistance coefficient Ct of the container ship. The prediction results show that the RBFNN prediction model still performs well. Good results can be obtained by RBFNN in interpolation prediction, even when using part of dimensionless features. Finally, the accuracy of the prediction method based on RBFNN is greatly improved compared with the modified admiralty coefficient.