Maritime transport plays a vital role in economic development. To establish a vessel scheduling model, accurate ship maneuvering models should be used to optimize the strategy and maximize the economic benefits. The use of nonparametric modeling techniques to identify ship maneuvering systems has attracted considerable attention. The Gaussian process has high precision and strong generalization ability in fitting nonlinear functions and requires less training data, which is suitable for ship dynamic model identification. Compared with other machine learning methods, the most obvious advantage of the Gaussian process is that it can provide the uncertainty of prediction. However, most studies on ship modeling and prediction do not consider the uncertainty propagation in Gaussian processes. In this paper, a moment-matching-based approach is applied to address the problem. The proposed identification scheme for ship maneuvering systems is verified by container ship simulation data and experimental data from the Workshop on Verification and Validation of Ship Maneuvering Simulation Methods (SIMMAN) database. The results indicate that the identified model is accurate and shows good generalization performance. The uncertainty of ship motion prediction is well considered based on the uncertainty propagation technology.

中文翻译：

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基于不确定性传播的高斯过程的船舶操纵运动识别与预测

海运在经济发展中起着至关重要的作用。建立船舶调度模型，需要使用准确的船舶操纵模型来优化策略，实现经济效益最大化。使用非参数建模技术来识别船舶操纵系统已经引起了相当大的关注。高斯过程在拟合非线性函数方面精度高，泛化能力强，训练数据量少，适用于船舶动力学模型识别。与其他机器学习方法相比，高斯过程最明显的优势在于它可以提供预测的不确定性。然而，大多数关于船舶建模和预测的研究没有考虑高斯过程中的不确定性传播。在本文中，应用基于矩匹配的方法来解决该问题。船舶操纵系统的拟议识别方案通过集装箱船模拟数据和船舶操纵模拟方法验证和验证研讨会（SIMMAN）数据库的实验数据进行验证。结果表明，识别出的模型是准确的，具有良好的泛化性能。基于不确定性传播技术，充分考虑了船舶运动预测的不确定性。结果表明，识别出的模型是准确的，具有良好的泛化性能。基于不确定性传播技术，充分考虑了船舶运动预测的不确定性。结果表明，识别出的模型是准确的，具有良好的泛化性能。基于不确定性传播技术，充分考虑了船舶运动预测的不确定性。