Weighted multi-kernel relevance vector machine for 3 DOF ship manoeuvring modeling with full-scale trial data

In order to improve the accuracy of ship maneuvering state prediction, a nonparametric identification method based on weighted multi-kernel relevance vector machine (WMRVM) is proposed to establish the motion prediction models of the vessel YUKUN. The WMRVM is composed of weight parameters and different types of kernel functions. On the one hand, RVM provides probability interpretation, its kernel functions do not have to satisfy Mercer condition; on the other hand, WMRVM improves the prediction accuracy of single kernel RVM. Taking the vessel YUKUN as the research plant, the full-scale trials satisfy the requirements of maneuverability test. The 20°/20° zigzag test data and 30° turning test data are used as the generalization verification. The prediction results obtained by the proposed algorithm are compared with those obtained by RVM and support vector regression (ε-SVR). The results show that the proposed algorithm has higher prediction accuracy and better generalization. This research can lay the foundation for the application of RVM in ship maneuvering state prediction and online identification modeling.