Signal prediction based on empirical mode decomposition and artificial neural networks

被引：2

作者：

Wang Yong ^{[1
]}

Liu Yanping ^{[2
]}

Yang Jing ^{[3
]}

机构：

[1] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Peoples R China

[2] Cent S Univ, Sch Civil Engn, Changsha 410075, Hunan, Peoples R China

[3] Hebei United Univ, Coll Mining Engn, Tangshan, Peoples R China

来源：

GEODESY AND GEODYNAMICS | 2012年 / 3卷 / 01期

关键词：

EMD (Empirical Mode Decomposition); ANN (Artificial Neural Networks); MRME (Most Relevant Matching Extension); IMF (Intrinsic Mode Function); endpoint problem; RBF (Radial Basis Function);

D O I：

10.3724/SP.J.1246.2012.00052

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

In view of the usefulness of Empirical Mode Decomposition (EMD), Artificial Neural Networks (ANN), and Most Relevant Matching Extension (MRME) methods in dealing with nonlinear signals, we propose a new way of combining these methods to deal with signal prediction. We found the results of combining EMD with either ANN or MRME to have higher prediction precision for a time series than the result of using EMD alone.

引用

页码：52 / 56

页数：5

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