Parametric Identification of Ship Maneuvering Models by Using Support Vector Machines
Parametric Identification of Ship Maneuvering Models by Using Support Vector Machines
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发表时间：
2009-03
影响因子：
1.4
通讯作者：
W. Luo;Z. Zou
中科院分区：
文献类型：
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作者：
W. Luo;Z. Zou
在船舶操纵运动数学模型中，系统辨识与模型试验或实船试验相结合是确定水动力系数的有效方法之一。通过分析舵角、纵荡速度、横摇速度、横摆角速度等数据，提出了一种基于支持向量机（SVM）的常规水面舰船水动力系数估计方法。系数包含在线性核函数的内积的展开式中。利用辨识出的参数对机动运动进行了预测。辨识和仿真结果表明了所提辨识算法的有效性。通过向训练样本引入额外的斜坡信号来减少同时的漂移和多重共线性。不同机动的运动变量的模拟和预测之间的比较表明，训练好的支持向量机具有良好的预测能力。
System identification combined with free-running model tests or full-scale trials is one of the effective methods to determine the hydrodynamic coefficients in the mathematical models of ship maneuvering motion. By analyzing the available data, including rudder angle, surge speed, sway speed, yaw rate, and so forth, a method based on support vector machines (SVM) to estimate the hydrodynamic coefficients is proposed for conventional surface ships. The coefficients are contained in the expansion of the inner product of a linear kernel function. Predictions of maneuvering motion are conducted by using the parameters identified. The results of identification and simulation demonstrate the validity of the identification algorithm proposed. The simultaneous drift and multicollinearity are diminished by introducing an additional ramp signal to the training samples. Comparison between the simulated and predicted motion variables from different maneuvers shows good predictive ability of the trained SVM.