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CiteWeb id: 19980000014

CiteWeb score: 13557

DOI: 10.1098/rspa.1998.0193

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 make 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.

The publication "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis" is placed in the Top 1000 of the best publications in CiteWeb. Also in the category Physics it is included to the Top 100. Additionally, the publicaiton "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis" is placed in the Top 100 among other scientific works published in 1998.
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