Nonparametric modeling of ship maneuvering motion based on Gaussian process regression optimized by genetic algorithm

Abstract

A novel method, Gaussian process regression optimized by genetic algorithm (GA-GPR), is proposed for nonparametric modeling of ship maneuvering motion. A genetic algorithm with adaptive crossover and mutation operations is introduced for adjustment and optimization of hyperparameters in the kernel function. The sliding time window method is adopted to update the training samples to improve the adaptability of the model. Taking the Mariner vessel and the container ship KCS as study objects, the simulation data and the real measured data provided by SIMMAN 2020 workshop are employed to evaluate the proposed method. The prediction results of the proposed method and the traditional Gaussian process regression are compared and validated against available experimental data of zigzag and turning circle maneuvers. It shows that the proposed method has higher prediction accuracy and better generalization ability, indicating that the hyperparameters optimized by genetic algorithm make the model closer to the ship's dynamic characteristics.