Experimental investigation of empirical mode decomposition by reduction of end effect error

2019 ◽  
Vol 534 ◽  
pp. 122171 ◽  
Author(s):  
S.H. Momeni Massouleh ◽  
S.A. Hosseini Kordkheili
Keyword(s):  
Experimental Investigation ◽  
Empirical Mode Decomposition ◽  
End Effect ◽  
Mode Decomposition

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.

scholarly journals Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Yukun Bao ◽  
Tao Xiong ◽  
Zhongyi Hu
Keyword(s):  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
End Effect ◽  
Modeling Framework ◽  
Passenger Traffic ◽  
Straightforward Application ◽  
Mode Decomposition ◽  
Vector Machines

With regard to the nonlinearity and irregularity along with implicit seasonality and trend in the context of air passenger traffic forecasting, this study proposes an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) modeling framework incorporating a slope-based method to restrain the end effect issue occurring during the shifting process of EEMD, which is abbreviated as EEMD-Slope-SVMs. Real monthly air passenger traffic series including six selected airlines in USA and UK were collected to test the effectiveness of the proposed approach. Empirical results demonstrate that the proposed decomposition and ensemble modeling framework outperform the selected counterparts such as single SVMs (straightforward application of SVMs), Holt-Winters, and ARIMA in terms of RMSE, MAPE, GMRAE, and DS. Additional evidence is also shown to highlight the improved performance while compared with EEMD-SVM model not restraining the end effect.

Fault Feature Extraction Based on Improved EEMD and Hilbert Transform

2011 ◽  
Vol 314-316 ◽  
pp. 1126-1130
Author(s):  
Pei Guo Hou ◽  
Qian Zhou ◽  
Zhong Dong Wang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
End Effect ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Fault Feature Extraction ◽  
Improved Eemd

Ensemble Empirical Mode Decomposition (EEMD) can overcome the mode mixing problem in Empirical Mode Decomposition (EMD) effectively. The Hilbert-Huang transform still exists end effect in applications, in order to improve the end effect, this paper put forward a method of fault feature extraction based on improved EEMD and Hilbert transform which combines support vector regression (SVR) machine with mirror extension to continue the signal. The analysis on simulation experiments results show that the method can restrain the end effect effectively, get a more accurate instantaneous frequency and instantaneous amplitude.

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