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Abstract: Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operations. Inspired by the satisfactory nonlinear learning capability of a support vector regression (SVR) model and the strong non-stationary processing ability of empirical mode decomposition (EMD), this paper develops a hybrid autoregressive (AR)-EMD-SVR model for the short-term forecast of nonlinear and non-stationary ship motion. The proposed hybrid model is designed by coupling the SVR model with an AR-EMD technique, which employs an AR model in ends extension. In addition to the AR-EMD-SVR model, the linear AR model, non-linear SVR model, and hybrid EMD-AR model are also studied for comparison by using ship motion time series obtained from model testing in a towing tank. Prediction results suggest that the non-stationary difficulty in the SVR model is overcome by using the AR-EMD technique, and better predictions are obtained by the proposed AR-EMD-SVR model than other models.

This is a very good article and the authors are to be congratulated.

用于非线性非平稳船舶运动极短期预报的一种复合自回归经验模态分解支持向量机回归模型

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