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A method for extracting chaotic signal from noisy environment

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Title: A method for extracting chaotic signal from noisy environment
Authors: Shang, Li-Jen1, Shyu, Kuo-Kai kkshyu@ee.ncu.edu.tw
Source: Chaos, Solitons & Fractals. Oct2009, Vol. 42 Issue 2, p1120-1125. 6p.
Subjects: Chaos theory, Signal detection, Random noise theory, Independent component analysis, Amplitude modulation, Mathematical optimization
Abstract: Abstract: In this paper, we propose a approach for extracting chaos signal from noisy environment where the chaotic signal has been contaminated by white Gaussian noise. The traditional type of independent component analysis (ICA) is capable of separating mixed signals and retrieving them independently; however, the separated signal shows unreal amplitude. The results of this study show with our method the real chaos signal can be effectively recovered. [Copyright &y& Elsevier]
Copyright of Chaos, Solitons & Fractals is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 43174999
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
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  Data: A method for extracting chaotic signal from noisy environment
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  Data: <searchLink fieldCode="AR" term="%22Shang%2C+Li-Jen%22">Shang, Li-Jen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Shyu%2C+Kuo-Kai%22">Shyu, Kuo-Kai</searchLink><i> kkshyu@ee.ncu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Chaos%2C+Solitons+%26+Fractals%22">Chaos, Solitons & Fractals</searchLink>. Oct2009, Vol. 42 Issue 2, p1120-1125. 6p.
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  Data: <searchLink fieldCode="DE" term="%22Chaos+theory%22">Chaos theory</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Random+noise+theory%22">Random noise theory</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+component+analysis%22">Independent component analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Amplitude+modulation%22">Amplitude modulation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
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  Data: Abstract: In this paper, we propose a approach for extracting chaos signal from noisy environment where the chaotic signal has been contaminated by white Gaussian noise. The traditional type of independent component analysis (ICA) is capable of separating mixed signals and retrieving them independently; however, the separated signal shows unreal amplitude. The results of this study show with our method the real chaos signal can be effectively recovered. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Chaos, Solitons & Fractals is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.chaos.2009.03.010
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      – Code: eng
        Text: English
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        PageCount: 6
        StartPage: 1120
    Subjects:
      – SubjectFull: Chaos theory
        Type: general
      – SubjectFull: Signal detection
        Type: general
      – SubjectFull: Random noise theory
        Type: general
      – SubjectFull: Independent component analysis
        Type: general
      – SubjectFull: Amplitude modulation
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
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      – TitleFull: A method for extracting chaotic signal from noisy environment
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            NameFull: Shang, Li-Jen
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            NameFull: Shyu, Kuo-Kai
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              Text: Oct2009
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              Y: 2009
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            – TitleFull: Chaos, Solitons & Fractals
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