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.
Highlights
By applying the fractional calculus to the ANNs, the researchers have found that the performance of the fractional-order models is better than integer-order ones, especially in the aspect of memory and hereditary
A nonfragile nonlinear fractional-order observer is designed in Discrete Dynamics in Nature and Society
[21] and an adaptive event-triggered scheme has been developed in [22]. These existing fractional-order systems employed event-triggered mechanism (ETM) are introduced with single delay or without only
Summary
By applying the fractional calculus to the ANNs, the researchers have found that the performance of the fractional-order models is better than integer-order ones, especially in the aspect of memory and hereditary. There are few related studies on the nonfragile SE for fractional-order neural network based on ETM with multiple time delays, which motivates us to shorten this gap. Inspired by the aforementioned lines, a nonfragile state estimator is designed for a class of fractional-order neural networks (FNNs) based on ETM.
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