Notice of RetractionEnd effects processing of Hilbert-Huang transform based on genetic algorithm and RBF Neural Network

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.

[1]  Zhu Wang,et al.  A research using hybrid RBF/Elman neural networks for intrusion detection system secure model , 2009, Comput. Phys. Commun..

[2]  Shuren Qin,et al.  A new envelope algorithm of Hilbert-Huang Transform , 2006 .

[3]  Hong Jiang,et al.  Optimal Design of Cognitive Radio Wireless Parameters based on Non-dominated Neighbor Distribution Genetic Algorithm , 2009, 2009 Eighth IEEE/ACIS International Conference on Computer and Information Science.

[4]  Junsheng Cheng,et al.  Application of support vector regression machines to the processing of end effects of Hilbert Huang transform , 2007 .

[5]  Yu Sheng-lin,et al.  Hilbert—Huang transform and its application , 2007 .

[6]  Shaoze Yan,et al.  A revised Hilbert–Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure , 2008 .

[7]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  Wenjin Gu,et al.  A three-dimensional proportional guidance law based on RBF neural network , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[9]  P. Torkzadeh,et al.  Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network , 2008 .

[10]  Xin-Jian Zhu,et al.  Short communication Modeling a SOFC stack based on GA-RBF neural networks identification , 2007 .

[11]  Xin-Jian Zhu,et al.  Predictive control of SOFC based on a GA-RBF neural network model , 2008 .