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

Xiaoguang Shao | Zendy; Ming Lyu | Zendy; Jie Zhang | Zendy

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

Author(s) -

Xiaoguang Shao,

Ming Lyu,

Jie Zhang

Publication year - 2021

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2021/6695353

Subject(s) - estimator , control theory (sociology) , artificial neural network , computer science , state estimator , stability theory , linear matrix inequality , mathematics , bandwidth (computing) , transmission (telecommunications) , mathematical optimization , statistics , telecommunications , artificial intelligence , nonlinear system , control (management) , quantum mechanics , physics

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.

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