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New Results of Globally Exponentially Attractive Set and Synchronization Controlling of the Qi Chaotic System

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Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6063))

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Abstract

This paper treats the globally exponentially attractive set and synchronization problem of the Qi chaotic system. Firstly, based on the generalized Lyapunov function theory, a new ellipsoid estimation of the globally exponentially attractive set and positive invariant set of the Qi chaotic system was given without existence assumptions. Secondly, based on some inequalities techniques and matrix theory, nonlinear feedback control with two inputs was used to realize the globally exponentially synchronization of two chaotic systems. Some sufficient algebraic criteria for the globally exponential synchronization of two chaotic systems are obtained analytically. Finally, numerical simulations are presented to show the effectiveness of the proposed chaos synchronization scheme.

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Author information

Authors and Affiliations

  1. Institute of Nonlinear and Complex Systems, China Three Gorges University, Yichang, Hubei, 443002, China

    Jigui Jian, Xiaolian Deng & Zhengwen Tu

Authors
  1. Jigui Jian
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  2. Xiaolian Deng
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  3. Zhengwen Tu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu

  3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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© 2010 Springer-Verlag Berlin Heidelberg

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Jian, J., Deng, X., Tu, Z. (2010). New Results of Globally Exponentially Attractive Set and Synchronization Controlling of the Qi Chaotic System. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_82

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  • DOI: https://doi.org/10.1007/978-3-642-13278-0_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13277-3

  • Online ISBN: 978-3-642-13278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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