On-line Modeling of Ship Maneuvering Motion based on Support Vector Machines
On-line Modeling of Ship Maneuvering Motion based on Support Vector Machines
复制标题
基于支持向量机的船舶操纵运动在线建模
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Yin Jian-Chuan
中科院分区：
文献类型：
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作者：
Xu Feng;Zou Zao-Jian;Yin Jian-Chuan
支持向量机（SVM）是一种先进的人工智能（AI）算法，它基于结构风险最小化（SRM）准则，具有很高的泛化性能。提出了一种增量式最小二乘支持向量机（LS-SVM）方法，并将其应用于船舶操纵运动的在线建模中，对线性Abkowitz模型中的粘性力导数和舵力导数进行了在线辨识。线，在自由行驶模型试验的基础上在线辨识了一阶线性响应模型中的K和T指标，并利用辨识出的响应模型对横摆角速度进行了预测，辨识和预测结果与模型试验数据吻合较好，验证了增量式LS-SVM在船舶操纵运动在线建模中的有效性。
Support Vector Machines(SVM) is an advanced artificial intelligence(AI) algorithm.It is based on the criteria of structural risk minimization(SRM) and has high generalization performance.In this paper,incremental least square Support Vector Machines(LS-SVM) is deduced and applied to on-line modeling of ship maneuvering motion.The viscous force derivatives and rudder force derivatives in the linear Abkowitz model are identified on-line based on the simulation test.K and T indexes in the first-order linear response model are identified on-line based on the free running model test,and yaw rate is predicted by the identified response model.The results of identification and prediction are all in good agreement with the model test data,which demonstrates the validity of incremental LS-SVM in on-line modeling of ship maneuvering motion.