一种用于非线性非平稳波浪极短期预报的复合经验模态分解自回归模型（英文）

发布时间：2018-08-24 10:18

【摘要】：目的:相对于由能量平衡方程得到的数值预报模型和以神经网络为代表的非线性模型而言,自回归(AR)模型在波浪预报中具有计算效率高、自适应性强和建模所需的样本小等优点,但同时存在局限于平稳线性假设的缺陷。针对非线性非平稳波浪的极短期预报问题,提出一种复合的经验模态分解自回归预报模型,提高波浪预报精度。创新点:1.研究非线性非平稳波浪极短期预报问题,提出一种复合的预报方法;2.基于三个不同地理位置的海洋波浪实测数据对预测模型进行验证,并分析非线性非平稳性对波浪预报结果的影响。方法:1.在AR模型中引入经验模态分解(EMD)方法,形成复合的EMD-AR预报模型;2.分析实测波浪数据的非线性和非平稳性特点,并基于实测波浪数据获得AR模型和EMD-AR模型的预报结果;3.基于多种预报误差度量分析AR模型和EMD-AR模型的预报性能以及非线性非平稳性对波浪预报结果的影响。结论:1.波浪非线性和非平稳性会导致AR预报模型精度降低。预报误差中,幅值上的偏差主要由波浪的非线性引起,而相位上的偏差则是源于波浪的非平稳性;2.EMD方法能够有效地克服波浪非线性和非平稳性对AR模型在精度上所带来的不良影响,在精度上EMD-AR模型的预报结果较AR模型有较大提高。
[Abstract]:Objective: compared with the numerical prediction model derived from the energy balance equation and the nonlinear model represented by neural network, the autoregressive (AR) model has high computational efficiency in wave prediction. It has the advantages of strong adaptability and small sample size for modeling, but it also has the limitation of stationary linear hypothesis. A compound empirical mode decomposition autoregressive prediction model is proposed to improve the prediction accuracy of nonlinear non-stationary waves. The innovation point is 1: 1. In this paper, the problem of nonlinear non-stationary wave extreme short-term prediction is studied, and a compound forecasting method is presented. The prediction model is verified based on the measured data of ocean waves at three different geographical locations, and the influence of nonlinear nonstationarity on the prediction results is analyzed. Method 1: 1. The empirical mode decomposition (EMD) method is introduced into the AR model to form a compound EMD-AR prediction model. The characteristics of nonlinearity and nonstationarity of measured wave data are analyzed, and the prediction results of AR model and EMD-AR model are obtained based on the measured wave data. The prediction performance of AR model and EMD-AR model as well as the influence of nonlinear nonstationarity on wave prediction results are analyzed based on multiple prediction error measures. Conclusion 1. The nonlinearity and nonstationarity of wave will reduce the precision of AR prediction model. In the prediction error, the deviation in amplitude is mainly caused by the nonlinearity of the wave. The deviation in phase is due to the non-stationarity of wave. 2. EMD method can effectively overcome the bad influence of wave nonlinearity and nonstationarity on the accuracy of AR model. The prediction result of EMD-AR model is better than that of AR model in precision.
【作者单位】： Department
【基金】：supported by the National Natural Science Foundation of China(Nos.51079032,51490671,and 11572093) the International Science and Cooperation Sponsored by the National Ministry of Science and Technology of China(No.2012DFA70420)
【分类号】：P731.33

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