首页|Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations
Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations
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This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with propor-tional delay and sensor saturations.In practical engineering,numerous unnecessary signals are transmitted to the estimator through the networks,which increases the burden of communication bandwidth.A dynamic event-triggered mechanism,instead of a static event-triggered mechanism,is employed to select useful data.By constructing a meaningful Lyapunov-Krasovskii functional,a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system.In the end,two numerical simulations are employed to illustrate the feasibil-ity and validity of the proposed theoretical results.
NETL
NSTL
万方数据
