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New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control

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  • Xiaofan Li
  • Yuan Ge
  • Hongjian Liu
  • Huiyuan Li
  • Jian-an Fang

Abstract

This paper addresses the synchronization issue for the drive-response fractional-order memristor‐based neural networks (FOMNNs) via state feedback control. To achieve the synchronization for considered drive-response FOMNNs, two feedback controllers are introduced. Then, by adopting nonsmooth analysis, fractional Lyapunov’s direct method, Young inequality, and fractional-order differential inclusions, several algebraic sufficient criteria are obtained for guaranteeing the synchronization of the drive-response FOMNNs. Lastly, for illustrating the effectiveness of the obtained theoretical results, an example is given.

Suggested Citation

  • Xiaofan Li & Yuan Ge & Hongjian Liu & Huiyuan Li & Jian-an Fang, 2020. "New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control," Complexity, Hindawi, vol. 2020, pages 1-11, September.
    • Handle: RePEc:hin:complx:2470972
    DOI: 10.1155/2020/2470972
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    Cited by:

    1. He, Jin-Man & Pei, Li-Jun, 2023. "Function matrix projection synchronization for the multi-time delayed fractional order memristor-based neural networks with parameter uncertainty," Applied Mathematics and Computation, Elsevier, vol. 454(C).
    2. Wang, Weiping & He, Chang & Wang, Zhen & Hramov, Alexander & Fan, Denggui & Yuan, Manman & Luo, Xiong & Kurths, Jürgen, 2021. "Dynamic analysis of synaptic loss and synaptic compensation in the process of associative memory ability decline in Alzheimer’s disease," Applied Mathematics and Computation, Elsevier, vol. 408(C).

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