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Synchronization-based parameter estimation of fractional-order neural networks

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  • Gu, Yajuan
  • Yu, Yongguang
  • Wang, Hu

Abstract

This paper focuses on the parameter estimation problem of fractional-order neural network. By combining the adaptive control and parameter update law, we generalize the synchronization-based identification method that has been reported in several literatures on identifying unknown parameters of integer-order systems. With this method, parameter identification and synchronization can be achieved simultaneously. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.

Suggested Citation

  • Gu, Yajuan & Yu, Yongguang & Wang, Hu, 2017. "Synchronization-based parameter estimation of fractional-order neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 351-361.
    • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:351-361
    DOI: 10.1016/j.physa.2017.04.124
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    References listed on IDEAS

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    1. Peng, Guojun & Jiang, Yaolin & Chen, Fang, 2008. "Generalized projective synchronization of fractional order chaotic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3738-3746.
    2. Wang, Sha & Yu, Yongguang & Diao, Miao, 2010. "Hybrid projective synchronization of chaotic fractional order systems with different dimensions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4981-4988.
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    Cited by:

    1. Zheng, Yi & Wu, Xiaoqun & Fan, Ziye & Wang, Wei, 2022. "Identifying topology and system parameters of fractional-order complex dynamical networks," Applied Mathematics and Computation, Elsevier, vol. 414(C).

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