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

doi:10.1088/1674-1056/25/1/010202

中国物理B ›› 2016, Vol. 25 ›› Issue (1): 10202-010202.doi: 10.1088/1674-1056/25/1/010202

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

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)

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

收稿日期:2015-07-16 修回日期:2015-08-30 出版日期:2016-01-05 发布日期:2016-01-05
通讯作者: Wen-Bo Wang E-mail:wwb0178@163.com
基金资助:
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).

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

Received:2015-07-16 Revised:2015-08-30 Online:2016-01-05 Published:2016-01-05
Contact: Wen-Bo Wang E-mail:wwb0178@163.com
Supported by:
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).

摘要/Abstract

摘要： 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.

关键词: independent component analysis, empirical mode decomposition, chaotic signal, denoising

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.

Key words: independent component analysis, empirical mode decomposition, chaotic signal, denoising

中图分类号: (Fourier analysis)

02.30.Nw

31.70.Hq (Time-dependent phenomena: excitation and relaxation processes, and reaction rates)

王文波, 张晓东, 常毓禅, 汪祥莉, 王钊, 陈希, 郑雷. Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating[J]. 中国物理B, 2016, 25(1): 10202-010202.

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[J]. Chin. Phys. B, 2016, 25(1): 10202-010202.

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

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

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