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- W4384103413 abstract "Highly accurate motion prediction is critical to facilitate safe navigation and autonomous control of a ship. In this paper, a math-data integrated prediction (MDIP) model of ship maneuvering motion is newly developed by combining with mathematical and data-driven modules. The math part is identified by employing variable-order hydrodynamic derivatives which are derived from Taylor expansion. Using extended Kalman filter, an optimal-order mathematical prediction model is obtained. The data-driven part dwells on prediction residuals which are approximated by a least square-support vector machine. Furthermore, integration mechanisms are devised to cohere mathematical and data-driven models as a whole, by deploying summation and neural network approximation, respectively. By comparisons, it is verified that not only order selection but also math-data integration can enhance ship motion prediction accuracy, for which various maneuvering tests are analyzed. The results demonstrate that the proposed MDIP model using math-data integration offers much stronger generalization, thereby paving a new path for ship maneuvering motion prediction." @default.
- W4384103413 created "2023-07-13" @default.
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- W4384103413 date "2023-10-01" @default.
- W4384103413 modified "2023-10-16" @default.
- W4384103413 title "Math-data integrated prediction model for ship maneuvering motion" @default.
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- W4384103413 doi "https://doi.org/10.1016/j.oceaneng.2023.115255" @default.
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