In this paper, a new method is proposed to identify the parameters of roll motion equation using the response data of ship in regular waves. The proposed method is based on the fact that if the estimated parameters are close to the true parameters, then, its response are also close to the true response. Therefore, the parameter identification is achieved via minimizing the mean square error between the true response and the estimated response. To effectively solving the parameter minimization problem, an improved imperialist competition algorithm (ICA), called opposition based learning Gaussian bare bone imperialist competition algorithm (OBL-GBBICA) is proposed. The proposed OBL-GBBICA integrates the opposition learning and Gaussian sampling technique into ICA to enhance its exploration ability and speed up its convergence. Experimental results show that the proposed method can accurately identify the parameters of roll motion equation. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

---

基于对偶学习高斯裸骨帝国主义竞争算法的规则波船舶横摇运动方程参数辨识

本文提出了一种利用规则波浪中船舶响应数据识别横摇运动方程参数的新方法。所提出的方法是基于这样一个事实，如果估计参数接近真实参数，那么它的响应也接近真实响应。因此，通过最小化真实响应和估计响应之间的均方误差来实现参数识别。为了有效解决参数最小化问题，提出了一种改进的帝国主义竞争算法（ICA），称为基于对立的学习高斯裸骨帝国主义竞争算法（OBL-GBBICA）。提出的 OBL-GBBICA 将反对学习和高斯采样技术集成到 ICA 中，以增强其探索能力并加速其收敛。实验结果表明，该方法能够准确识别横摇运动方程的参数。© 2021 日本电气工程师学会。由 Wiley Periodicals LLC 出版。