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Event-Triggered State Estimation for Fractional-Order Neural Networks

Author

Listed:
  • Bingrui Xu

    (School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China)

  • Bing Li

    (School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract

This paper is concerned with the problem of event-triggered state estimation for a class of fractional-order neural networks. An event-triggering strategy is proposed to reduce the transmission frequency of the output measurement signals with guaranteed state estimation performance requirements. Based on the Lyapunov method and properties of fractional-order calculus, a sufficient criterion is established for deriving the Mittag–Leffler stability of the estimation error system. By making full use of the properties of Caputo operator and Mittag–Leffler function, the evolution dynamics of measured error is analyzed so as to exclude the unexpected Zeno phenomenon in the event-triggering strategy. Finally, two numerical examples and simulations are provided to show the effectiveness of the theoretical results.

Suggested Citation

  • Bingrui Xu & Bing Li, 2022. "Event-Triggered State Estimation for Fractional-Order Neural Networks," Mathematics, MDPI, vol. 10(3), pages 1-15, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:325-:d:730240
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    Cited by:

    1. Xinggui Li & Ruofeng Rao & Shouming Zhong & Xinsong Yang & Hu Li & Yulin Zhang, 2022. "Impulsive Control and Synchronization for Fractional-Order Hyper-Chaotic Financial System," Mathematics, MDPI, vol. 10(15), pages 1-13, August.
    2. Shuoting Wang & Kaibo Shi & Jin Yang, 2022. "Improved Stability Criteria for Delayed Neural Networks via a Relaxed Delay-Product-Type Lapunov–Krasovskii Functional," Mathematics, MDPI, vol. 10(15), pages 1-14, August.

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