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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.

Notas/Comentarios de Juan Ignacio Godino:
En este trabajo se presenta un método para el análisis de datos no estacionarios y no lineales. El método, llamado Descomposición Empírica en Modos (Empirical Mode Decomposition -EMD) descompone las señales en un conjunto de secuencias -llamadas funciones modo intrínsecas-, que permiten desentrañar la cuasi-periodicidad y las características ocultas de los datos. Es un método adaptativo de descomposición de la señal, lo que lo convierte en una técnica muy adecuada para el análisis de datos no lineales y no estacionarios. Esta técnica ha sido ampliamente utilizada en aplicaciones biomédicas, de análisis de series financieras, procesado de imagen, aplicaciones meteorológicas, geológicas, en neurociencias, epidemiología...


  • Autor/es: 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.
  • Fecha: 1998-03
  • Publicado en: Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences. 08 March 1998, Volume 454. Issue 1971, Pages 903-995.
  • Idioma: Inglés
  • Formato: PDF
  • Contribución: Juan Ignacio Godino Llorente.
  • Palabras clave: Inteligencia computacional y artificial, Ordenadores y tratamiento de la información, Proceso de señal

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