DocumentCode
1949613
Title
Notice of Retraction
End effects processing of Hilbert-Huang transform based on genetic algorithm and RBF Neural Network
Author
Jinghui Ma ; Hong Jiang ; Li Yao ; Song Pu
Author_Institution
Inf. Inst., Southwest Univ. of Sci. & Technol. MianYang, Mianyang, China
Volume
4
fYear
2010
fDate
9-11 July 2010
Firstpage
312
Lastpage
316
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The problem of end effects in Hilbert-Huang transform is produced in the Empirical Mode Decomposition (EMD), which has a badly effect on Hilbert-Huang transform. In order to overcome this problem, multi-objective Genetic Algorithm (GA) for solving the parameters selection of RBF Neural Network (RBF_NN) (GRHHT) is presented in this paper. Then the RBF_NN is used to predict the signal before EMD. The scheme can effectively resolve the end effects. The simulation results from the typical definite signals demonstrate that the problem of end effects in Hilbert Huang transform could be resolved effectively, and its performance is better than prediction methods by RBF neural network and support Vector Machine (SVM), respectively.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The problem of end effects in Hilbert-Huang transform is produced in the Empirical Mode Decomposition (EMD), which has a badly effect on Hilbert-Huang transform. In order to overcome this problem, multi-objective Genetic Algorithm (GA) for solving the parameters selection of RBF Neural Network (RBF_NN) (GRHHT) is presented in this paper. Then the RBF_NN is used to predict the signal before EMD. The scheme can effectively resolve the end effects. The simulation results from the typical definite signals demonstrate that the problem of end effects in Hilbert Huang transform could be resolved effectively, and its performance is better than prediction methods by RBF neural network and support Vector Machine (SVM), respectively.
Keywords
Hilbert transforms; end effectors; genetic algorithms; radial basis function networks; signal processing; Hilbert-Huang transform; RBF neural network; empirical mode decomposition; end effect processing; multiobjective genetic algorithm; nonstationary signal processing; Artificial neural networks; Support vector machines; Hilbert-Huang transform; genetic algorithm; neural network; support vector machin;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564604
Filename
5564604
Link To Document