基于互补自适应噪声的集合经验模式分解算法

doi:10.11999/JEIT141632

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Abstract

Empirical Model Decomposition (EMD) and its improved algorithms are most useful signal processing methods. However, those methods still lack rigorous mathematical theory. This paper attempts to analyze mathematically the reconstruction errors for Ensemble EMD (EEMD) and EEMD with Adaptive Noises (EEMDAN). Moreover, the formulae of the residual noise are deduced step by step. There exists the residual noise in each intrinsic mode function during the EEMDAN. To suppress the residual noise, an improved ensemble empirical mode decomposition with complementary adaptive noises by adding pairs of positive and negative noises is proposed. The experimental results indicate that the proposed method can obviously reduce the residual noise in each intrinsic mode function compared with the EEMD and the EEMDAN, and it also has better signal reconstruction precision and faster signal decomposition.

Key words： Empirical Model Decomposition (EMD) Ensemble EMD (EEMD) EEMD with Adaptive Noise (EEMDAN) Mode mixing

Received: 25 December 2014 Published: 17 July 2015

PACS:

TN911.7

Fund:

The National Natural Science Foundation of China (61001179, 61471132); The Project on Integration of Production, Education, and Research, Dongguan, Guangdong Province, China(2013509104105); The Guangzhou Science & Technology Key Project on?Collaborative Innovation in Integration of Production, Education, and Research(201508010001)

Corresponding Authors: Cai Nian E-mail: cainian@gdut.edu.cn

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	Cai Nian
	Huang Wei-wei
	Xie Wei
	Ye Qian
	Yang Zhi-jing

Cite this article:

Cai Nian,Huang Wei-wei,Xie Wei等. Ensemble Empirical Mode Decomposition Base on Complementary Adaptive Noises[J]. JEIT, 2015, 37(10): 2383-2389.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT141632 OR http://jeit.ie.ac.cn/EN/Y2015/V37/I10/2383

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