当前位置: X-MOL 学术J. Navigation. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
An improved nonlinear innovation-based parameter identification algorithm for ship models
The Journal of Navigation ( IF 2.4 ) Pub Date : 2021-03-05 , DOI: 10.1017/s0373463321000102
Baigang Zhao , Xianku Zhang

To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

中文翻译:

一种改进的基于非线性创新的船模参数辨识算法

为解决用最少的试验数据快速、准确地识别船模参数的问题,提出一种船模非线性创新参数识别算法。这是基于非线性反正切函数,它可以在原始随机梯度算法的基础上处理创新。在船上进行了模拟于鹏使用26组测试数据比较了最小二乘算法、原始随机梯度算法和改进随机梯度算法的参数识别能力。结果表明,改进后的算法与最小二乘算法相比,参数识别的准确率提高了约12%。通过对船舶的仿真进一步验证了算法的有效性于坤. 结果证实了该算法能够在相对较小的数据集的基础上快速生成高度准确的参数识别。该方法可以扩展到只有少量测试数据可用的其他参数识别系统。
更新日期:2021-03-05
down
wechat
bug