文献类型：期刊文献

英文题名：Significant Wave Height Prediction Based on MSFD Neural Network

作者：Wang, Huan[1,2]; Fu, Dongyang[3]; Liao, Shan[2,3]; Wang, Guancheng[2,3]; Xiao, Xiuchun[2,3]

机构：[1] School of Oceanography and Meteorology, Guangdong Ocean University, Zhanjiang, China; [2] Shenzhen Institute, Guangdong Ocean University, Shenzhen, China; [3] School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China

年份：2019

起止页码：39

外文期刊名：10th International Conference on Intelligent Control and Information Processing, ICICIP 2019

收录：EI(收录号：20201308354664)

语种：英文

外文关键词：Forecasting - Functions - Neural network models - Decomposition - Oceanography

外文摘要：Due to the complicated behavior of the ocean wave, significant wave height (SWH) prediction is a difficult field in physical oceanography. In this paper, a novel neural network model, based on multiple sine functions decomposition (MSFD), is exploited to achieve the prediction of SWH. Different from traditional models built on physical processes of wave generation and dissipation, the method presented in this paper predicts and analyzes SWH from a mathematical statistical perspective. In particular, the variation rules of the SWH are learned by decomposing the mapping from time to SWH into a plurality of sine functions, and then the new data are predicted by linear combination of these sine functions. Correlation analysis and error between the forecast data and the actual data indicate that the MSFD neural network performs well in predicting SWH data. ? 2019 IEEE.