摘要
Based on the ε - support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. The 10°/10°, 20°/20° zigzag tests and the 35° turning circle manoeuvre are simulated. Part of the simulation data for the 20°/20° zigzag test are used to train the support vectors, and the trained support vector machine is used to predict the whole 20° / 20° zigzag test. Comparison between the simula- ted and predicted 20° / 20° zigzag test shows a good predictive ability of the three modelling methods. Then all mathematical models obtained by the modelling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the modelling methods are analyzed and compared with each other in terms of the application conditions, the prediction accuracy and the computation speed. An appropriate modelling method can be chosen according to the intended use of the mathematical models and the available data for the system identification.
Based on the ε - support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. The 10°/10°, 20°/20° zigzag tests and the 35° turning circle manoeuvre are simulated. Part of the simulation data for the 20°/20° zigzag test are used to train the support vectors, and the trained support vector machine is used to predict the whole 20° / 20° zigzag test. Comparison between the simula- ted and predicted 20° / 20° zigzag test shows a good predictive ability of the three modelling methods. Then all mathematical models obtained by the modelling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the modelling methods are analyzed and compared with each other in terms of the application conditions, the prediction accuracy and the computation speed. An appropriate modelling method can be chosen according to the intended use of the mathematical models and the available data for the system identification.
基金
Project supported by the National Natural Science Foundation of China(Grant No.51279106)
the Special Research Fund for the Doctoral Program of Higher Education of China(Grant No.20110073110009)
