Article(id=1311360873134915903, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757606400000, receivedDateStr=2025-09-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582396062, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582396062, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582396062, creator=13701087609, updateTime=1790582396062, updator=13701087609, issue=Issue{id=1311360859318870426, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='4', pageStart='1', pageEnd='185', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790582392768, creator='13701087609', updateTime=1790583829226, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311366884331716830, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311366884331716831, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=12, endPage=20, ext={EN=ArticleExt(id=1311360873365602624, articleId=1311360873134915903, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Grey-box modeling of ship maneuvering motion based on AE-LS-SVM, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

This study aims to improve the accuracy of ship motion prediction by proposing a gray-box modeling method for ship maneuvering motion, termed AE-LS-SVM, which combines the Alpha Evolution (AE) algorithm to optimize the Least Squares Support Vector Machine (LS-SVM). The AE algorithm's superior global search capability was employed to optimize the hyperparameters of LS-SVM. The dataset in this study was obtained through ship maneuvering tests conducted using the Marine Systems Simulator (MSS). Based on the Abkowitz model, a three-Degree-of-Freedom (3-DOF) gray-box model of ship maneuvering motion was constructed, incorporating maneuvering variables and ship responses. The gray-box model established in this study was then subjected to comparative analysis with gray-box models developed using GWO-LS-SVM (Grey Wolf Optimizer) and PSO-LS-SVM (Particle Swarm Optimization) through maneuvering tests. Experimental results demonstrate that the AE-LS-SVM method achieves reductions in Mean Squared Error (MSE) of approximately 6% and 4%, reductions in Mean Absolute Percentage Error (MAPE) of 3.84% and 1.96%, and improvements in correlation coefficient (CC) of 0.13% and 0.04%, respectively, compared to the two benchmark methods. Additionally, the anti-interference capability of the proposed method was validated by introducing varying levels of noise into the training dataset to simulate measurement errors. The findings indicate that the AE-LS-SVM method exhibits high modeling accuracy and robust noise resistance, offering valuable support for ship motion prediction.

, authors=Qiang Yuan^{1, *}, Zhihong Liu¹, Jinzhi Peng¹, Yujie Jiang¹, Fei Dong¹, Ruiping Zhou², authorsList=Qiang Yuan, Zhihong Liu, Jinzhi Peng, Yujie Jiang, Fei Dong, Ruiping Zhou, authorCompany=null, correspAuthors=Qiang Yuan, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311360873768255809, articleId=1311360873134915903, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于AE-LS-SVM的船舶操纵运动灰箱建模方法, columnId=1240655021528904650, journalTitle=中国航海, columnName=智能航运, runingTitle=null, highlight=null, articleAbstract=

为提高船舶运动预测的准确性，本文提出了一种基于Alpha进化算法（AE）优化最小二乘支持向量机（LS-SVM）的船舶运动灰箱建模方法（AE-LS-SVM）。该方法利用AE优秀的全局搜索能力对LS-SVM超参数进行寻优，在Abkowitz模型的基础上，构建包含操纵变量和船舶响应的三自由度船舶操纵运动灰箱模型。通过海洋模拟器进行船舶操纵试验获得数据集，将本研究所建立的灰箱模型与GWO-LS-SVM和PSO-LS-SVM建立的灰箱模型进行操纵试验对比分析。试验结果表明，AE-LS-SVM方法在模型输出结果的均方误差（MSE）上分别降低了约6%和4%，平均绝对百分比误差（MAPE）分别降低了约3.84%和1.96%，相关系数（CC）分别提高了0.13%和0.04%。此外，通过在训练数据集中引入不同水平的干扰以模拟系统测量误差，进一步验证了该方法的抗干扰性能。研究结果表明，AE-LS-SVM方法具有较高的建模精度和良好的抗干扰性，可以为船舶运动预测提供一定程度的支持。

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