Signal prediction based on empirical mode decomposition and artificial neural networks

作     者：Wang Yong Liu Yanping Yang Jing 

作者机构：School of Surveying ＆ Land Information Engineering Henan Polytechnic University Jiaozuo 454000 China School of Civil Engineering Central South University Changsha 410075 China College of Mining Engineering Hebei United University Tangshan 063009 China 

出 版 物：《Geodesy and Geodynamics》 (大地测量与地球动力学（英文版）)

年 卷 期：2012年第3卷第1期

页      面：52-56页

学科分类：0711[理学-系统科学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supporteal by the Notional Natural Scince Foundation of Hebei Province(D201000921) 

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

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 pro- pose 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.