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Identification of Chaotic Systems with Application to Chaotic Communication |
| FENG Jiu-Chao1,2;QIU Yu-Hui3 |
| 1Faculty of Electronic and Information Engineering, Southwest China Normal University, Chongqing 400715
2Faculty of Electronics and Information, South China University of Technology, Guangzhou 510641
3Department of Computer Science, Southwest China Normal University, Chongqing 400715 |
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| Cite this article: |
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FENG Jiu-Chao, QIU Yu-Hui 2004 Chin. Phys. Lett. 21 250-253 |
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Abstract We propose and develop a novel method to identify a chaotic system with time-varying bifurcation parameters via an observation signal which has been contaminated by additive white Gaussian noise. This method is based on an adaptive algorithm, which takes advantage of the good approximation capability of the radial basis function neural network and the ability of the extended Kalman filter for tracking a time-varying dynamical system. It is demonstrated that, provided the bifurcation parameter varies slowly in a time window, a chaotic
dynamical system can be tracked and identified continuously, and the time-varying bifurcation parameter can also be retrieved in a sub-window of time via a simple least-square-fit method.
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| Keywords:
05.45.-a
05.45.Vx
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Published: 01 February 2004
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| PACS: |
05.45.-a
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(Nonlinear dynamics and chaos)
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05.45.Vx
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(Communication using chaos)
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