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Non-fragile state estimation for delayed fractional-order memristive neural networks

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  • Li, Ruoxia
  • Gao, Xingbao
  • Cao, Jinde

Abstract

The issue of non-fragile estimation for fractional-order memristive system is provided in this paper. By endowing the Lyapunov technique, the corresponding works that ensuring the globally asymptotic stability of the error model are presented, which can be calculated efficiently. In the end, the analytical methods are voiced by two simulations.

Suggested Citation

  • Li, Ruoxia & Gao, Xingbao & Cao, Jinde, 2019. "Non-fragile state estimation for delayed fractional-order memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 221-233.
    • Handle: RePEc:eee:apmaco:v:340:y:2019:i:c:p:221-233
    DOI: 10.1016/j.amc.2018.08.031
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    References listed on IDEAS

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    1. Gallegos, Javier A. & Duarte-Mermoud, Manuel A., 2016. "On the Lyapunov theory for fractional order systems," Applied Mathematics and Computation, Elsevier, vol. 287, pages 161-170.
    2. 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.
    3. Lien, Chang-Hua, 2007. "H∞ non-fragile observer-based controls of dynamical systems via LMI optimization approach," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 428-436.
    4. Li, Ruoxia & Cao, Jinde, 2016. "Stability analysis of reaction-diffusion uncertain memristive neural networks with time-varying delays and leakage term," Applied Mathematics and Computation, Elsevier, vol. 278(C), pages 54-69.
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    Citations

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    Cited by:

    1. Li, Ruoxia & Gao, Xingbao & Cao, Jinde, 2019. "Quasi-state estimation and quasi-synchronization control of quaternion-valued fractional-order fuzzy memristive neural networks: Vector ordering approach," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    2. Jiang, Ling & Cao, Jinde & Xiong, Lianglin, 2019. "Generalized multiobjective robustness and relations to set-valued optimization," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 599-608.
    3. Liu, An & Huang, Xia & Fan, Yingjie & Wang, Zhen, 2021. "A control-interval-dependent functional for exponential stabilization of neural networks via intermittent sampled-data control," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    4. Karnan, A. & Nagamani, G., 2022. "Non-fragile state estimation for memristive cellular neural networks with proportional delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 217-231.
    5. Zhang, Tianwei & Li, Yongkun, 2022. "S-asymptotically periodic fractional functional differential equations with off-diagonal matrix Mittag-Leffler function kernels," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 331-347.
    6. Deng, Jie & Li, Hong-Li & Cao, Jinde & Hu, Cheng & Jiang, Haijun, 2023. "State estimation for discrete-time fractional-order neural networks with time-varying delays and uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    7. M. Syed Ali & Gani Stamov & Ivanka Stamova & Tarek F. Ibrahim & Arafa A. Dawood & Fathea M. Osman Birkea, 2023. "Global Asymptotic Stability and Synchronization of Fractional-Order Reaction–Diffusion Fuzzy BAM Neural Networks with Distributed Delays via Hybrid Feedback Controllers," Mathematics, MDPI, vol. 11(20), pages 1-24, October.
    8. Yao, Xueqi & Zhong, Shouming, 2021. "EID-based robust stabilization for delayed fractional-order nonlinear uncertain system with application in memristive neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).

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