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Adaptive fuzzy synchronization of discrete-time chaotic systems

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  • Vasegh, Nastaran
  • Majd, Vahid Johari

Abstract

This paper presents a fuzzy model-based adaptive approach to synchronize two different discrete-time chaotic systems. Takagi–Sugeno (TS) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, an adaptive law is derived to estimate its unknown parameters. Then, a control law is proposed to stabilize the error dynamics. The design procedure is illustrated by two examples to demonstrate the effectiveness of the proposed methodology.

Suggested Citation

  • Vasegh, Nastaran & Majd, Vahid Johari, 2006. "Adaptive fuzzy synchronization of discrete-time chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 28(4), pages 1029-1036.
    • Handle: RePEc:eee:chsofr:v:28:y:2006:i:4:p:1029-1036
    DOI: 10.1016/j.chaos.2005.08.123
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    Cited by:

    1. Wang, Jiang & Si, Wenjie & Li, Huiyan, 2009. "Robust ISS-satisficing variable universe indirect fuzzy control for chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 39(1), pages 28-38.
    2. Hung, Meei-Ling & Lin, Jui-Sheng & Yan, Jun-Juh & Liao, Teh-Lu, 2008. "Optimal PID control design for synchronization of delayed discrete chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 35(4), pages 781-785.
    3. Vasegh, Nastaran & Majd, Vahid Johari, 2009. "Fuzzy model-based adaptive synchronization of time-delayed chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 40(3), pages 1484-1492.
    4. Pan, Shing-Tai & Lai, Chih-Chin, 2008. "Identification of chaotic systems by neural network with hybrid learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 233-244.

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