Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support

The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis

Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung and Henry H. Liu
Proceedings: Mathematical, Physical and Engineering Sciences
Vol. 454, No. 1971 (Mar. 8, 1998), pp. 903-995
Published by: Royal Society
Stable URL: http://www.jstor.org/stable/53161
Page Count: 93
  • Get Access
  • Read Online (Free)
  • Cite this Item
If you need an accessible version of this item please contact JSTOR User Support
The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis
Preview not available

Abstract

A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the 'empirical mode decomposition' method with which any complicated data set can be decomposed into a finite and often small number of 'intrinsic mode functions' that admit well-behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes. With the Hilbert transform, the 'instrinic mode functions' yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert spectrum. In this method, the main conceptual innovations are the introduction of 'intrinsic mode functions' based on local properties of the signal, which makes the instantaneous frequency meaningful; and the introduction of the instantaneous frequencies for complicated data sets, which eliminate the need for spurious harmonics to represent nonlinear and non-stationary signals. Examples from the numerical results of the classical nonlinear equation systems and data representing natural phenomena are given to demonstrate the power of this new method. Classical nonlinear system data are especially interesting, for they serve to illustrate the roles played by the nonlinear and non-stationary effects in the energy-frequency-time distribution.

Page Thumbnails

  • Thumbnail: Page 
903
    903
  • Thumbnail: Page 
904
    904
  • Thumbnail: Page 
905
    905
  • Thumbnail: Page 
906
    906
  • Thumbnail: Page 
907
    907
  • Thumbnail: Page 
908
    908
  • Thumbnail: Page 
909
    909
  • Thumbnail: Page 
910
    910
  • Thumbnail: Page 
911
    911
  • Thumbnail: Page 
912
    912
  • Thumbnail: Page 
913
    913
  • Thumbnail: Page 
914
    914
  • Thumbnail: Page 
915
    915
  • Thumbnail: Page 
916
    916
  • Thumbnail: Page 
917
    917
  • Thumbnail: Page 
918
    918
  • Thumbnail: Page 
919
    919
  • Thumbnail: Page 
920
    920
  • Thumbnail: Page 
921
    921
  • Thumbnail: Page 
922
    922
  • Thumbnail: Page 
923
    923
  • Thumbnail: Page 
924
    924
  • Thumbnail: Page 
925
    925
  • Thumbnail: Page 
926
    926
  • Thumbnail: Page 
927
    927
  • Thumbnail: Page 
928
    928
  • Thumbnail: Page 
929
    929
  • Thumbnail: Page 
930
    930
  • Thumbnail: Page 
931
    931
  • Thumbnail: Page 
932
    932
  • Thumbnail: Page 
933
    933
  • Thumbnail: Page 
934
    934
  • Thumbnail: Page 
935
    935
  • Thumbnail: Page 
936
    936
  • Thumbnail: Page 
937
    937
  • Thumbnail: Page 
938
    938
  • Thumbnail: Page 
939
    939
  • Thumbnail: Page 
940
    940
  • Thumbnail: Page 
941
    941
  • Thumbnail: Page 
942
    942
  • Thumbnail: Page 
943
    943
  • Thumbnail: Page 
944
    944
  • Thumbnail: Page 
945
    945
  • Thumbnail: Page 
946
    946
  • Thumbnail: Page 
947
    947
  • Thumbnail: Page 
948
    948
  • Thumbnail: Page 
949
    949
  • Thumbnail: Page 
950
    950
  • Thumbnail: Page 
951
    951
  • Thumbnail: Page 
952
    952
  • Thumbnail: Page 
953
    953
  • Thumbnail: Page 
954
    954
  • Thumbnail: Page 
955
    955
  • Thumbnail: Page 
956
    956
  • Thumbnail: Page 
957
    957
  • Thumbnail: Page 
958
    958
  • Thumbnail: Page 
959
    959
  • Thumbnail: Page 
960
    960
  • Thumbnail: Page 
961
    961
  • Thumbnail: Page 
962
    962
  • Thumbnail: Page 
963
    963
  • Thumbnail: Page 
964
    964
  • Thumbnail: Page 
965
    965
  • Thumbnail: Page 
966
    966
  • Thumbnail: Page 
967
    967
  • Thumbnail: Page 
968
    968
  • Thumbnail: Page 
969
    969
  • Thumbnail: Page 
970
    970
  • Thumbnail: Page 
971
    971
  • Thumbnail: Page 
972
    972
  • Thumbnail: Page 
973
    973
  • Thumbnail: Page 
974
    974
  • Thumbnail: Page 
975
    975
  • Thumbnail: Page 
976
    976
  • Thumbnail: Page 
977
    977
  • Thumbnail: Page 
978
    978
  • Thumbnail: Page 
979
    979
  • Thumbnail: Page 
980
    980
  • Thumbnail: Page 
981
    981
  • Thumbnail: Page 
982
    982
  • Thumbnail: Page 
983
    983
  • Thumbnail: Page 
984
    984
  • Thumbnail: Page 
985
    985
  • Thumbnail: Page 
986
    986
  • Thumbnail: Page 
987
    987
  • Thumbnail: Page 
988
    988
  • Thumbnail: Page 
989
    989
  • Thumbnail: Page 
990
    990
  • Thumbnail: Page 
991
    991
  • Thumbnail: Page 
992
    992
  • Thumbnail: Page 
993
    993
  • Thumbnail: Page 
994
    994
  • Thumbnail: Page 
995
    995