新型蝙蝠算法辨识船舶运动模型参数

为准确、有效辨识出船舶运动模型的参数,以构建精确的船舶运动模型,将群智能优化算法中新型蝙蝠算法引入船舶运动模型的参数辨识。将船舶运动模型参数辨识问题转化为一个多维变量函数优化问题,分析了新型蝙蝠算法求解船舶运动模型参数辨识的适应性;给出了采用新型蝙蝠算法进行船舶运动模型参数辨识的流程。基于实船实验数据,采用新型蝙蝠算法辨识了实船一阶线型响应型模型参数。实例显示,将船舶运动模型参数辨识问题转化成优化问题,通过新型蝙蝠算法对构建的误差准则目标函数的优化,能够快速找到使得目的函数最优的一组变量,该变量即为辨识得到的模型参数。研究表明,提出的技术路线简明且适用,是一种有效的计算机辅助船舶运动模型参数离线辨识方案。

Parameter identification of ship motion model by using novel bat algorithm

In order to accurately and effectively identify the parameters constructing a mathematical ship motion model, the novel bat algorithm (NBA), one kind of swarm intelligence optimization algorithm, is introduced to the parameter identification of ship motion model. Transforming the parameter identification issue into a problem of multivariate function optimization, NBA"s adaptability to solve the problem is analyzed; Technique process to identify suited parameters using the algorithm is proposed. Taking parameter identification of a first order linear response ship motion model for an example, the practical research shows that, NBA can optimize the error criterion based objective function quickly by searching an optimal set of variables, which are corresponding to the parameters to be identified. The process and results show that proposed technical route is simple and feasible with strong adaptability, making it an effective computer aided off-line parameter identification method for mathematical ship motion model.