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Complete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays

Author

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  • Huaiqin Wu
  • Luying Zhang
  • Sanbo Ding
  • Xueqing Guo
  • Lingling Wang

Abstract

This paper investigates the complete periodic synchronization of memristor-based neural networks with time-varying delays. Firstly, under the framework of Filippov solutions, by using M -matrix theory and the Mawhin-like coincidence theorem in set-valued analysis, the existence of the periodic solution for the network system is proved. Secondly, complete periodic synchronization is considered for memristor-based neural networks. According to the state-dependent switching feature of the memristor, the error system is divided into four cases. Adaptive controller is designed such that the considered model can realize global asymptotical synchronization. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.

Suggested Citation

  • Huaiqin Wu & Luying Zhang & Sanbo Ding & Xueqing Guo & Lingling Wang, 2013. "Complete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-12, July.
    • Handle: RePEc:hin:jnddns:140153
    DOI: 10.1155/2013/140153
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    References listed on IDEAS

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    1. James M. Tour & Tao He, 2008. "The fourth element," Nature, Nature, vol. 453(7191), pages 42-43, May.
    2. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
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    Cited by:

    1. Shi, Kaibo & Wang, Jun & Zhong, Shouming & Zhang, Xiaojun & Liu, Yajuan & Cheng, Jun, 2019. "New reliable nonuniform sampling control for uncertain chaotic neural networks under Markov switching topologies," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 169-193.
    2. Liu, Shuxin & Yu, Yongguang & Zhang, Shuo & Zhang, Yuting, 2018. "Robust stability of fractional-order memristor-based Hopfield neural networks with parameter disturbances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 845-854.

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