Identification of ship steering dynamics based on ACA-SVR

According to the high-order nonlinearity and parameter uncertainty of the ship steering dynamics, it is difficult to establish the accurate mathematical model by using normal identification methods. To solve this problem, a new kind of support vector regression based on the ant colony algorithm (ACA-SVR) is proposed. This method can select the parameters of SVR automatically without trial and error, thus ensure the accuracy of parameters optimization. Applying this method in the model identification of the ship steering dynamics, and comparing the identification effect with the experimental reference data. The SVR obtained by this method is able to establish the system model effectively, the structure is simple and generalization ability is well.