【计算机科学】【2017】人工神经网络在波浪特性预测中的应用

本文为近东大学（作者：YAZID SALEM）的硕士论文，共135页。

本研究应用Bretschneider谱和Sverdrup-Munk Bretschneider（SMB）提出的方程，利用风速和持续时间、水和空气温度差以及风区长度等记录数据，模拟了波浪特征（波高和周期）。在所有海洋结构物分析中，通过开发一个模拟波浪和海流作用于海洋结构物构件的程序来估算波浪和海流产生的力是至关重要的。基于Airy波理论（线性理论）对工程应用的吸引力，本研究采用了Airy波理论（线性理论）。莫里森方程用于将速度和加速度项转换为合力。为了校准和比较的目的，我们对照一个著名的专业软件包“结构分析计算机系统”（SACS）对开发的程序进行检查。此外，对于所提出模型的改进以及对确定性模型的替代，仍然存在很大的空间。因此，本研究探讨利用目前比较流行的人工神经网络（ANN）技术来达到此目的的可能性。此外，将神经网络模型与基于SMB和Bretschneider方程的两种特征预测方法进行了比较，结果表明ANN模型比SMB和Bretschneider方程具有更好的性能。在训练过程中使用了不同参数的数据集，采用了不同的神经网络结构。研究结果证实，一个经过适当训练的网络可以在更广阔的开放区域提供可接受的结果，包括当采样和预测间隔较大（一周量级时）。

In this study the wave characteristics (height and period of wave) were simulated by applying the Bretschneider spectrum and equations presented by Sverdrup-MunkBretschneider (SMB) by using the recorded data such as wind velocity and duration, differences between water and air temperature and the fetch length. It is essential for all offshore structures analysis to estimate the forces generated by the wave and current by developing a program for modeling wave and current forces on offshore structural members. Airy wave theory (linear theory) has been implemented in the present study, based on its attractiveness for engineering use. The Morison equation was used for converting the velocity and acceleration terms into resultant forces. For calibration and for comparison purposes, a developed program was checked against a well-known professional software package called Structural Analysis Computer System (SACS). Furthermore, a wide range still exists to improve the presented models as well as provides alternative to deterministic models. Therefore, this study investigates the possibility of utilizing the relatively current technique of artificial neural networks (ANN) for this purpose. Besides, the comparison of ANN models with the two characteristic prediction methods based on equations of SMB and Bretschneider equations showed a better performance for ANN models rather than SMB and Bretschneider equations. Different ANN architectures were used to by using sets of data with different parameters used in training process. The results confirm that a suitably trained network might supply acceptable outcomes in open wider areas, as well as when the sampling and predicting interval is enormous in order of magnitude of a week.

1. 引言
2. 文献回顾
3. 研究方法
4. 结果与讨论
5. 结论与未来展望

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标签：计算机科学,ANN,人工神经网络,models,Bretschneider,2017,wave,equations,SMB
来源： https://blog.csdn.net/weixin_42825609/article/details/111183537