Comparison and Optimization of the Parameter Identification Technique for Estimating Ship Response Models

Parameter identification techniques assorted from the system identification technology is a sufficient and commonly used approach for estimating the parameters of ship dynamic models. It is not tough to find an identification method to identify the parameters of linear or nonlinear ship dynamic models, but how to select a suitable parameter identification approach with high accuracy and low complexity for special cases is necessary to be studied. This contribution aims at determining a relatively suitable parameter identification method for estimation ship response models via selecting and comparing one intelligent method with the classic least squares method (LS) from a methodological point of view. Support vector machines (SVM) as an intelligent method is chosen because it is a kind of batch identification technique requiring no initial estimation of identified parameters. For well-confirming parameters in SVM, the artificial bee colony (ABC) algorithm instead of the empirical method is used to optimize the parameters in SVM. With the measurement zigzag test data from a scaled-model ship as training and verification samples, the maneuvering indices of ship response models are respectively identified using LS and SVM, and the verification of the identified models are sufficiently proceeded through comparing the prediction and measurement results. It is shown that the two different categories of ship response models are analytically and numerically consistent with each other. Comparison between the measured and predicted maneuvers demonstrates that SVM optimized by ABC algorithm is also an effective parameter identification technique.