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Nonfragile Estimator Design for Fractional-Order Neural Networks under Event-Triggered Mechanism

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  • Xiaoguang Shao
  • Ming Lyu
  • Jie Zhang
  • Wangyan Li

Abstract

This paper is concerned with the nonfragile state estimation for a kind of delayed fractional-order neural network under the event-triggered mechanism (ETM). To reduce the bandwidth occupation of the communication network, the ETM is employed in the sensor-to-estimator channel. Moreover, in order to reflect the reality, the transmission delay is taken into account in the model establishment. Sufficient criteria are supplied to make sure that the augmented system is asymptotically stable by using the fractional-order Lyapunov indirect approach and the linear matrix inequality method. In the end, the theoretical result is shown by means of two numerical examples.

Suggested Citation

  • Xiaoguang Shao & Ming Lyu & Jie Zhang & Wangyan Li, 2021. "Nonfragile Estimator Design for Fractional-Order Neural Networks under Event-Triggered Mechanism," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-12, March.
    • Handle: RePEc:hin:jnddns:6695353
    DOI: 10.1155/2021/6695353
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