Event-Triggered State Estimation for Fractional-Order Neural Networks

Bingrui Xu and Bing Li
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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

Mathematics, 2022, vol. 10, issue 3, 1-15

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.

Keywords: fractional-order neural networks; state estimation; Mittag–Leffler stability; event-triggered mechanism; zeno phenomenon (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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