文献类型：期刊文献

英文题名：Real-time prediction of metacentric height of ro-ro passenger ships in Qiongzhou strait based on improved RBF neural network

作者：Wang, Lijun[1];Liao, Shenghao[1];Wang, Sisi[1];Jia, Baozhu[2];Yin, Jianchuan[2];Li, Ronghui[2]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2024

卷号：312

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001327685000001)、、EI(收录号：20244017123382)、Scopus(收录号：2-s2.0-85205007465)、WOS

基金：This work was partially supported by National Science Foundation of China (Grant NO. 52171346 and 52271361) , the Fund of Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching (Grant NO. 2023B1212030003) and the Key Area Project of Ordinary Universities in Guangdong Province (Grant NO. 2024ZDZX3054) .

语种：英文

外文关键词：Ro-ro passenger ship; RBF neural network; Metacentric height prediction; Bayesian optimization; Grey correlation analysis

外文摘要：Addressing the complexities and real-time challenges in calculating ship metacentric height (GM), this study proposes an improved method using an optimized radial basis function neural network (RBFNN) for real-time GM prediction. Bayesian optimization is introduced to fine-tune the hyperparameters of the RBFNN, aiming to enhance the model's generalization performance. The study focuses on the Qiongzhou Strait Ro-ro passenger ship 'Zijing No.11' and selects three GM-related factors as neural network inputs using grey correlation analysis. The GM calculated by the empirical formula serves as the expected value, which is compared with predictions from various algorithms. Simulation results demonstrate that the improved RBFNN achieves significantly lower prediction errors compared to the unoptimized version. Furthermore, compared to other machine learning models and artificial neural networks, the proposed model exhibits superior performance in predicting ship initial stability height. Consequently, this model offers a practical tool for accurate and real-time GM prediction, enhancing intelligent stowage and operational efficiency in shipping.