Hydrodynamic coefficients identification of ship simplified modular model based on support vector regression

Objectives To address the issue of multicollinearity and parameter drift in the identification of hydrodynamic coefficients in ship separated-type models, this paper proposes a method for modeling simplified three-degree-of-freedom modular models based on support vector regression (SVR).

Methods Initially, a processing strategy is introduced to enhance the effectiveness of the sample data. Further, Lasso regression is introduced to select the most influential hydrodynamic coefficients and alleviate multicollinearity. Subsequently, a regression model for the identification of hydrodynamic derivatives is derived for the MMG model. A data centralization and differencing method is then employed to reconstruct the regression model, mitigating the impact of parameter drift on hydrodynamic derivative identification errors.

Results Simulation experiments demonstrate good agreement between the hydrodynamic coefficient forecast values and numerical simulation results. The calculated values of root mean square error (RMSE) and correlation coefficient (CC) fall within a favorable range.

Conclusions The SVR algorithm successfully identifies the hydrodynamic derivatives of the modular model, the identified hydrodynamic coefficients exhibit high accuracy, and the established model demonstrates good predictive capability and robustness.