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Synchronization of fractional-order memristor-based complex-valued neural networks with uncertain parameters and time delays

Author

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  • Yang, Xujun
  • Li, Chuandong
  • Huang, Tingwen
  • Song, Qiankun
  • Huang, Junjian

Abstract

This paper talks about the global asymptotical synchronization problem of delayed fractional-order memristor-based complex-valued neural networks with uncertain parameters. Under the framework of Filippov solution and differential inclusion theory, several sufficient criteria ensuring the global asymptotical synchronization for the addressed drive-response models are derived, by means of Lyapunov direct method and comparison theorem. In addition, two numerical examples are designed to verify the correctness and effectiveness of the theoretical results.

Suggested Citation

  • Yang, Xujun & Li, Chuandong & Huang, Tingwen & Song, Qiankun & Huang, Junjian, 2018. "Synchronization of fractional-order memristor-based complex-valued neural networks with uncertain parameters and time delays," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 105-123.
    • Handle: RePEc:eee:chsofr:v:110:y:2018:i:c:p:105-123
    DOI: 10.1016/j.chaos.2018.03.016
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    References listed on IDEAS

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    5. Yang, Xujun & Li, Chuandong & Huang, Tingwen & Song, Qiankun, 2017. "Mittag–Leffler stability analysis of nonlinear fractional-order systems with impulses," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 416-422.
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