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Gradient method for blind chaotic signal separation based on proliferation exponent |
Lü Shan-Xiang (吕善翔), Wang Zhao-Shan (王兆山), Hu Zhi-Hui (胡志辉), Feng Jiu-Chao (冯久超) |
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China |
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Abstract A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis (FastICA) method when noise contamination is considerable.
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Received: 13 June 2013
Revised: 24 July 2013
Accepted manuscript online:
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PACS:
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05.45.-a
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(Nonlinear dynamics and chaos)
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05.40.Ca
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(Noise)
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05.45.Vx
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(Communication using chaos)
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Fund: Project supported by the National Natural Science Foundation of China (Grant No. 60872123), the Joint Fund of the National Natural Science Foundation and the Natural Science Foundation of Guangdong Province, China (Grant No. U0835001), the Fundamental Research Funds for the Central Universities of China (Grant No. 2012ZM0025), the South China University of Technology, China, and the Fund for Higher-Level Talents in Guangdong Province, China (Grant No. N9101070). |
Corresponding Authors:
Feng Jiu-Chao
E-mail: fengjc@scut.edu.cn
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Cite this article:
Lü Shan-Xiang (吕善翔), Wang Zhao-Shan (王兆山), Hu Zhi-Hui (胡志辉), Feng Jiu-Chao (冯久超) Gradient method for blind chaotic signal separation based on proliferation exponent 2014 Chin. Phys. B 23 010506
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[1] |
Feng J C and Tse C K 2008 Reconstruction of Chaotic Signals with Applications to Chaos-based Communications (Beijing: Tsinghua University Press) pp. 3–20
|
[2] |
Li W L, Li S F and Li G 2012 Chin. Phys. B 21 064217
|
[3] |
Luo Y L and Du M H 2013 Chin. Phys. B 22 080503
|
[4] |
Arena P, Buscarino A, Fortuna L and Frasca M 2006 Phys. Rev. E 74 026212
|
[5] |
Xie Z B and Feng J C 2010 IEEE Trans. Circuits Syst. II Exp. Briefs 57 461
|
[6] |
Liu K, Li H, Dai X and Xu P 2005 J. Inf. Comput. Sci. 2 283
|
[7] |
Hu Z H and Feng J C 2011 Acta Phys. Sin. 60 070505 (in Chinese)
|
[8] |
Wang S Y and Feng J C 2012 Acta Phys. Sin. 61 170508 (in Chinese)
|
[9] |
Popivanov D, Jivkova S, Stomonyakov V and Nicolova G 2005 Signal Process. 85 2112
|
[10] |
Chen H B, Feng J C and Fang Y 2008 Chin. Phys. Lett. 25 405
|
[11] |
Kuraya M, Uchida A, Sano S, Yoshimori S and Umeno K 2008 Electron. Lett. 44 248
|
[12] |
Hyvarinen A, Karhunen J and Oja E 2001 Independent Component Analysis (New York: John Wiley & Sons) pp. 147–237
|
[13] |
Hyvarinen A and Oja E 1997 Neural Comput. 9 1483
|
[14] |
Bell A J and Sejnowski T J 1995 Neural Comput. 7 1129
|
[15] |
Yang H H and Amari S I 1997 Neural Comput. 9 1457
|
[16] |
Ikeda S and Toyama K 2000 Neural Networks 13 1063
|
[17] |
Ikeda S 2000 ICA on Noisy Data: A Factor Analysis Approach (London: Springer) pp. 201–215
|
[18] |
Takens F 1981 Lecture Notes in Mathematics 898 366
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