{"title":"基于优化支持向量机的船舶操纵运动建模","authors":"W. Luo, Wenlong Cai","doi":"10.1109/ICICIP.2014.7010302","DOIUrl":null,"url":null,"abstract":"Support Vector Machines (SVM) based system identification is proposed to determine the hydrodynamic coefficients in the mathematical model of ship manoeuvring motion. To reduce the complexity of the mathematical model, sensitivity analysis is performed. Particle Swarm Optimization (PSO) is employed to obtain the optimal regularization factor in SVM. Combined with free model tests, the hydrodynamic coefficients are identified by using the optimized SVM Comparison between the prediction results and the test results demonstrates the validity of the modeling method proposed.","PeriodicalId":408041,"journal":{"name":"Fifth International Conference on Intelligent Control and Information Processing","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Modeling of ship manoeuvring motion using optimized support vector machines\",\"authors\":\"W. Luo, Wenlong Cai\",\"doi\":\"10.1109/ICICIP.2014.7010302\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Support Vector Machines (SVM) based system identification is proposed to determine the hydrodynamic coefficients in the mathematical model of ship manoeuvring motion. To reduce the complexity of the mathematical model, sensitivity analysis is performed. Particle Swarm Optimization (PSO) is employed to obtain the optimal regularization factor in SVM. Combined with free model tests, the hydrodynamic coefficients are identified by using the optimized SVM Comparison between the prediction results and the test results demonstrates the validity of the modeling method proposed.\",\"PeriodicalId\":408041,\"journal\":{\"name\":\"Fifth International Conference on Intelligent Control and Information Processing\",\"volume\":\"78 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fifth International Conference on Intelligent Control and Information Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICIP.2014.7010302\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fifth International Conference on Intelligent Control and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2014.7010302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling of ship manoeuvring motion using optimized support vector machines
Support Vector Machines (SVM) based system identification is proposed to determine the hydrodynamic coefficients in the mathematical model of ship manoeuvring motion. To reduce the complexity of the mathematical model, sensitivity analysis is performed. Particle Swarm Optimization (PSO) is employed to obtain the optimal regularization factor in SVM. Combined with free model tests, the hydrodynamic coefficients are identified by using the optimized SVM Comparison between the prediction results and the test results demonstrates the validity of the modeling method proposed.