Please wait a minute...
Chin. Phys. B, 2016, Vol. 25(1): 010202    DOI: 10.1088/1674-1056/25/1/010202
GENERAL Prev   Next  

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

Wen-Bo Wang(王文波)1,2, Xiao-Dong Zhang(张晓东)2, Yuchan Chang(常毓禅)3, Xiang-Li Wang(汪祥莉)4, Zhao Wang(王钊)5, Xi Chen(陈希)5, Lei Zheng(郑雷)6
1. College of Science, Wuhan University of Science and Technology, Wuhan 430065, China;
2. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China;
3. School of Finance, Renmin University of China, Beijing 100872, China;
4. School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063, China;
5. College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China;
6. Wuhan NARI Limited Liability Company of Sate Grid Electric Power Research Institute, Wuhan 430074, China
Abstract  In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.
Keywords:  independent component analysis      empirical mode decomposition      chaotic signal      denoising  
Received:  16 July 2015      Revised:  30 August 2015      Accepted manuscript online: 
PACS:  02.30.Nw (Fourier analysis)  
  31.70.Hq (Time-dependent phenomena: excitation and relaxation processes, and reaction rates)  
Fund: Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).
Corresponding Authors:  Wen-Bo Wang     E-mail:

Cite this article: 

Wen-Bo Wang(王文波), Xiao-Dong Zhang(张晓东), Yuchan Chang(常毓禅), Xiang-Li Wang(汪祥莉), Zhao Wang(王钊), Xi Chen(陈希), Lei Zheng(郑雷) Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating 2016 Chin. Phys. B 25 010202

[1] Li G L and Chen X Y 2008 Acta Electron. Sin. 36 1814 (in Chinese)
[2] Vicha T and Dohnal M 2008 Chaos Solitons Fractals 38 70
[3] Xie Z B and Feng J C 2010 Chin. Phys. B 19 050510
[4] Lu S X, Wang Z S, Hu Z H and Feng J C 2014 Chin. Phys. B 23 010506
[5] Dedieu H and Kisel A 1999 Int. J. Circuit Theory Appl. 27 577
[6] Jako Z and Kis G 2000 IEEE Trans. Circuits Syst.-I: Fundamental Theory and Applications 47 1720
[7] Schreiber T 1993 Phys. Rev. E 47 2401
[8] Han M, Liu Y H, Shi Z W and Xiang M 2007 Journal of System Simulation 19 364 (in Chinese)
[9] Leontitsis A, Bountis T and Pagge J 2004 Chaos 14 106
[10] Li G M and Lu S X 2015 Acta Phys. Sin. 64 160502 (in Chinese)
[11] Wang M J, Wu Z T and Feng J C 2015 Acta Phys. Sin. 64 040503 (in Chinese)
[12] Murguia J S and Canton C E 2006 Revista Mexicana De Fisica 52 155
[13] Liu Y X, Yang G S and Jia Q 2011 Acta Electron. Sin. 39 13 (in Chinese)
[14] Zhang L, Bao P and Wu X 2005 IEEE Transactions on Circuits and Systems for Video Technology 15 469
[15] Zhang H, Chen X H and Yang H Y 2011 Oil Geophysical Prospecting 46 70
[16] Huang N E, Shen Z and Long S R 1998 Proc. R. Soc. Lond. A 454 903
[17] Boudraa A and Cexus J 2007 IEEE Trans. on Instrumentation and Measurement 56 2196
[18] Olufemi A, Vladimir A and Auroop R 2011 IEEE Sensors Journal 11 2565
[19] Kopsinis Y and Mclaughli S 2009 IEEE Trans. Signal Process. 57 1351
[20] Zhang Q and Xing H Y 2015 Acta Electron. Sin. 43 901 (in Chinese)
[21] Wang X F, Qu J L, Gao F, Zhou Y P and Zhang X Y 2014 Acta Phys. Sin. 63 170203 (in Chinese)
[22] Hyvarinen A and Oja E 2000 Neural Networks 13 411
[23] Zhang Z S, Yu J, Yan Q, Meng Y S and Zhao Z 2011 Acta Geodaetica et Cartographica Sinica 40 289
[24] Li H and Sun Y L 2007 Journal of Beijing University of Posts and Telecommunications 30 33
[25] Zhao H W and Norden E H 2004 Proc. R. Soc. Lond. A 460 1597
[1] Chaotic signal denoising algorithm based on sparse decomposition
Jin-Wang Huang(黄锦旺), Shan-Xiang Lv(吕善翔), Zu-Sheng Zhang(张足生), Hua-Qiang Yuan(袁华强). Chin. Phys. B, 2020, 29(6): 060505.
[2] Adaptive optimization on ultrasonic transmission tomography-based temperature image for biomedical treatment
Yun-Hao Zhu(朱昀浩), Jie Yuan(袁杰), Stephen Z Pinter, Oliver D Kripfgans, Qian Cheng(程茜), Xue-Ding Wang(王学鼎), Chao Tao(陶超), Xiao-Jun Liu(刘晓峻), Guan Xu(徐冠), Paul L Carson. Chin. Phys. B, 2017, 26(6): 064301.
[3] Quantitative analysis of ammonium salts in coking industrial liquid waste treatment process based on Raman spectroscopy
Ya-Nan Cao(曹亚南), Gui-Shi Wang(王贵师), Tu Tan(谈图), Ting-Dong Cai(蔡廷栋), Kun Liu(刘锟), Lei Wang(汪磊), Gong-Dong Zhu(朱公栋), Jiao-Xu Mei(梅教旭). Chin. Phys. B, 2016, 25(10): 107403.
[4] Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter
Huang Jin-Wang, Feng Jiu-Chao. Chin. Phys. B, 2014, 23(7): 070504.
[5] Applicability of initial optimal maternal and fetal electrocardiogram combination vectors to subsequent recordings
Yan Hua-Wen, Huang Xiao-Lin, Zhao Ying, Si Jun-Feng, Liu Tie-Bing, Liu Hong-Xing. Chin. Phys. B, 2014, 23(11): 118702.
[6] Gradient method for blind chaotic signal separation based on proliferation exponent
Lü Shan-Xiang, Wang Zhao-Shan, Hu Zhi-Hui, Feng Jiu-Chao. Chin. Phys. B, 2014, 23(1): 010506.
[7] Denoising via truncated sparse decomposition
Xie Zong-Bo, Feng Jiu-Chao. Chin. Phys. B, 2011, 20(5): 050504.
[8] A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition
Fu Mao-Jing, Zhuang Jian-Jun, HouFeng-Zhen, Zhan Qing-Bo, Shao Yi, Ning Xin-Bao. Chin. Phys. B, 2010, 19(5): 058701.
[9] Filtering noisy chaotic signal via sparse representation based on random frame dictionary
Xie Zong-Bo, FengJiu-Chao. Chin. Phys. B, 2010, 19(5): 050510.
[10] On the climate prediction of nonlinear and non-stationary time series with the EMD method
Wan Shi-Quan, Feng Guo-Lin, Dong Wen-Jie, Li Jian-Ping, Gao Xin-Quan, He Wen-Ping. Chin. Phys. B, 2005, 14(3): 628-633.
[11] Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA
You Rong-Yi, Chen Zhong. Chin. Phys. B, 2005, 14(11): 2176-2180.
No Suggested Reading articles found!