Exponential state estimation for competitive neural network via stochastic sampled-data control with packet losses
Articles
Xin Sui
Jiangnan University
Yongqing Yang
Jiangnan University
Fei Wang
Qufu Normal University
https://orcid.org/0000-0002-2952-4911
Published 2020-07-01
https://doi.org/10.15388/namc.2020.25.17803
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Keywords

exponential state estimation
competitive neural network
sampled-data control
packet losses

How to Cite

SuiX., YangY. and WangF. (2020) “Exponential state estimation for competitive neural network via stochastic sampled-data control with packet losses”, Nonlinear Analysis: Modelling and Control, 25(4), pp. 523–544. doi: 10.15388/namc.2020.25.17803.

Abstract

This paper investigates the exponential state estimation problem for competitive neural networks via stochastic sampled-data control with packet losses. Based on this strategy, a switched system model is used to describe packet dropouts for the error system. In addition, transmittal delays between neurons are also considered. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled-data estimator with probabilistic sampling in two sampling periods is proposed. Then the estimator is designed in terms of the solution to a set of linear matrix inequalities (LMIs), which can be solved by using available software. When the missing of control packet occurs, some sufficient conditions are obtained to guarantee that the exponentially stable of the error system by means of constructing an appropriate Lyapunov function and using the average dwell-time technique. Finally, a numerical example is given to show the effectiveness of the proposed method.

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