Event-triggered Non-fragile H∞ State Estimation for Fuzzy Time-Delay Neural Networks

doi:10.16182/j.issn1004731x.joss.201806042

Abstract

Abstract: For a class of fuzzy neural networks with randomly occurring time-varying delays and randomly data packet loss, an event-triggered non-fragile H_∞ state estimator is designed. The event-triggered condition is introduced to determine whether the signal is transmitted or not, so as to reduce the occupation rate of network resource. Random variables of Gaussian distribution and the multiplicative gain uncertainties are adopted to construct the non-fragile state estimator with randomly occurring gain variations. By constructing Lyapunov function, and via stochastic computation and linear matrix inequality technique, the sufficient conditions for the existence of non-fragile estimators are obtained, which guarantee the asymptotical stability and the H_∞ performance constraint of dynamic estimation error system. After solving the linear matrix inequality, the gains of the estimator are obtained. A simulation example is given to illustrate the feasibility of the state estimator.

Key words: state estimation, non-fragile, time-varying delays, event-triggered, packet dropouts

CLC Number:

TP273

Wang Yanqin, Ren Weijian. Event-triggered Non-fragile H_∞ State Estimation for Fuzzy Time-Delay Neural Networks[J]. Journal of System Simulation, 2018, 30(6): 2335-2345.

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Event-triggered Non-fragile H_∞ State Estimation for Fuzzy Time-Delay Neural Networks

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