Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations  

作　　者：邵晓光 张捷 鲁延娟 Xiao-Guang Shao;Jie Zhang;Yan-Juan Lu(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China;School of Electronics and Information Engineering,Changchun University of Science and Technology,Changchun 130022,China)

机构地区：[1]School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China [2]School of Electronics and Information Engineering,Changchun University of Science and Technology,Changchun 130022,China

出　　处：《Chinese Physics B》2024年第7期126-135,共10页中国物理B（英文版）

摘　　要：This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional 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 feasibility and validity of the proposed theoretical results.

关 键 词：memristor-based neural networks proportional delays dynamic event-triggered mechanism sensor saturations 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP212[自动化与计算机技术—控制科学与工程]