Abstract:
Objectives To address the issue of multicollinearity and parameter drift in the identification of hydrodynamic coefficients in ship separated-type models, a method for modeling simplified three-degree-of-freedom modular models based on support vector regression (SVR) is proposed.
Methods Initially, processing strategy is introduced to enhance the effectiveness of the sample data. Further, introducing Lasso regression to select the most influential hydrodynamic coefficients and alleviate multicollinearity. Subsequently, a regression model for hydrodynamic derivatives identification is derived for MMG model. Data centralization and differencing method are employed to reconstruct the regression model, mitigating the impact of parameter drift on hydrodynamic derivatives identification errors.
Results Simulation experiments demonstrate good agreement between 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 hydrodynamic derivatives of the modular models, the identified hydrodynamic coefficients exhibit high accuracy, and the established model demonstrates good predictive capability and robustness.
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