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Parameter identification and synchronization for uncertain network group with different structures

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  • Li, Chengren
  • Lü, Ling
  • Sun, Ying
  • Wang, Ying
  • Wang, Wenjun
  • Sun, Ao

Abstract

We design a novel synchronization technique to research the synchronization of network group constituted of uncertain networks with different structures. Based on Lyapunov theorem, the selection principles of the control inputs and the parameter identification laws of the networks are determined, and synchronization conditions of the network group are obtained. Some numerical simulations are provided to verify the correctness and effectiveness of the synchronization technique. We find that the network number, the number of network nodes and network connections indeed will not affect the stability of synchronization of network group.

Suggested Citation

  • Li, Chengren & Lü, Ling & Sun, Ying & Wang, Ying & Wang, Wenjun & Sun, Ao, 2016. "Parameter identification and synchronization for uncertain network group with different structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 624-631.
    • Handle: RePEc:eee:phsmap:v:457:y:2016:i:c:p:624-631
    DOI: 10.1016/j.physa.2016.03.094
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    References listed on IDEAS

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    1. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2013. "Delay-induced synchronization transitions in small-world neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5473-5480.
    2. Yu, Haitao & Wang, Jiang & Liu, Qiuxiang & Sun, Jianbing & Yu, Haifeng, 2013. "Delay-induced synchronization transitions in small-world neuronal networks with hybrid synapses," Chaos, Solitons & Fractals, Elsevier, vol. 48(C), pages 68-74.
    3. Zheng, Yan Hong & Lu, Qi Shao, 2008. "Spatiotemporal patterns and chaotic burst synchronization in a small-world neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3719-3728.
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    Cited by:

    1. Zhou, Wenjia & Hu, Yuanfa & Liu, Xiaoyang & Cao, Jinde, 2022. "Finite-time adaptive synchronization of coupled uncertain neural networks via intermittent control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    2. Li, Wenlin & Gebremariam, Tesfay & Li, Chong & Song, Heshan, 2017. "Synchronization effect for uncertain quantum networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 621-627.
    3. Lü, Ling & Li, Chengren & Li, Gang & Sun, Ao & Yan, Zhe & Rong, Tingting & Gao, Yan, 2017. "Design of synchronization technique for uncertain discrete network group with diverse structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 543-551.
    4. Geng, Liang & Xiao, Renbin, 2017. "Outer synchronization and parameter identification approach to the resilient recovery of supply network with uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 407-421.

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