A new processing method for the end effect problem of empirical mode decomposition

2010 ◽  
Author(s):  
Haiyan Quan ◽  
Zengli Liu ◽  
Xinling Shi
Keyword(s):  
Empirical Mode Decomposition ◽  
Processing Method ◽  
End Effect ◽  
Mode Decomposition ◽  
New Processing

Transmission Line Fault Location Based on Hilbert-Huang Transformation and SVM

2013 ◽  
Vol 333-335 ◽  
pp. 1673-1678
Author(s):  
Ke Qin Bao ◽  
Bao Xing Wu ◽  
Yun Hui Xu
Keyword(s):  
Support Vector Machine ◽  
Transmission Line ◽  
Empirical Mode Decomposition ◽  
Fault Location ◽  
Signal Sequence ◽  
Hilbert Transformation ◽  
Support Vector ◽  
End Effect ◽  
Positioning Accuracy ◽  
Mode Decomposition

In the process of the Hilbert-Huang Transformation, empirical mode decomposition (EMD) and Hilbert Transformation of the IMF components may result in the terminal effect, utilizing the support vector machine (SVM) extend the signal sequence and IMF components to weaken the end effect. The paper analyzes the fault signal which extracted under the different fault conditions to complete the fault location. The simulation result shows that using SVM can effectively restrain terminal effect; In the different fault states can have a high positioning accuracy.

End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning

2011 ◽  
Vol 55-57 ◽  
pp. 407-412 ◽  
Author(s):  
Ye Yuan ◽  
Zhong Kai Yang ◽  
Qing Fu Li
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Input Signal ◽  
Inductive Reasoning ◽  
Qualitative Modeling ◽  
End Effect ◽  
Predictive Capability ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Simulation Results

This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring. The simulation results have shown that the fuzzy inductive reasoning based method has equivalent performance to the neural network based method.

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