An Improved Tidal Current Separation Method of Radial Basis Function Using Gradient Training
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摘要: 针对传统潮流分析方法在感潮河段实施走航数据潮流分离中的不足，提出了一种基于梯度训练法的径向基函数潮流分离方法，解决了传统潮流分析方法时序数据长、潮流分离实施复杂问题；也解决了基于贪婪拟合法的径向基函数潮流分离算法存在的节点位置无法准确确定以及过度拟合导致结果不稳定等难题；根据潮流分离结果重构流场，在徐六泾走航断面实验中取得了优于0.25 m/s的外推精度。Abstract: Ship-mounted acoustic Doppler current profiler (ADCP) measurements have been used to obtain detailed observations of the spatial patterns of flows. To separate tidal and subtidal currents from the ship-mounted ADCP data at tidal reach, traditional harmonic analysis method require redundant measurements, and complex tidal current separation operations. Developments in radial basis function (RBF) interpolation theory are demonstrated to significantly improve the quality of the tidal velocity field extracted from the measurements. Tidal current separation method of RBF using greedy fit couldn't optimal centers for RBF, and the over-fitting of RBF would lead to the instability of separation model. To overcome these deficiencies, this paper proposes an improved tidal current separation method of radial basis function using gradient training. The tidal current separation results in Xuliujing section verified the feasibility of the tidal current separation method of gradient training RBF. The inner precision of reconstructed flow field based on the tidal current separation method using gradient training is better than 0.21 m/s, and the prediction accuracy is better than 0.25 m/s.
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Keywords:
- current separation /
- tidal reach /
- ship-mounted ADCP /
- radial basis function /
- gradient training
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图 2 传统方法与本文方法所得余流和M2分潮潮流椭圆长半轴比较
Figure 2. Comparison of Residual and Ellipse Semimajor of M2 with Traditional Harmonic Analysis Method and Gradient Training Method
表 1 三种潮流分离方法的精度对比/(m·s-1)
Table 1 Comparison of Accuracies of Tidal Current Separation of Three Methods(m·s-1)
实验 拟合方法 内符合精度(±) 外推精度(±) 1 传统调和分析方法
径向基函数法(贪婪拟合法)
径向基函数法(梯度训练法)0.191
0.145
0.202/
/
/2 径向基函数法(贪婪拟合法)
径向基函数法(梯度训练法)0.113
0.1500.308
0.2153 径向基函数法(贪婪拟合法)
径向基函数法(梯度训练法)0.108
0.1500.345
0.246
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