Journal of Marine Science and Engineering Article Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process Yanjun Liu 1,2,†, Yifan Xue 1,2,*,† , Shuting Huang 1,2,*, Gang Xue 1,2 and Qianfeng Jing 3 1 Institute of Marine Science and Technology, School of Mechanical Engineering, Shandong University, Qingdao 266237, China;
[email protected] (Y.L.);
[email protected] (G.X.) 2 Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266200, China 3 Navigation College, Dalian Maritime University, Dalian 116026, China;
[email protected] * Correspondence:
[email protected] (Y.X.);
[email protected] (S.H.) † These authors contributed equally to this work and should be considered co-first authors. Abstract: Reducing the carbon emissions of ships and increasing the utilization of marine renewable energy are the important ways to achieve the goal of carbon neutrality in ocean engineering. Estab- lishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of operation energy efficiency optimization and prediction of power generation. System identification from observed input–output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a sin- gle modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, Citation: Liu, Y.; Xue, Y.; Huang, S.; three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian Xue, G.; Jing, Q.