控制理论(社会学)
计算机科学
估计员
人工神经网络
状态估计器
国家(计算机科学)
指数稳定性
事件(粒子物理)
带宽(计算)
理论(学习稳定性)
线性矩阵不等式
数学
数学优化
算法
人工智能
非线性系统
机器学习
电信
控制(管理)
统计
物理
量子力学
作者
Xiaoguang Shao,Jie Zhang,Yanjuan Lu
标识
DOI:10.1088/1674-1056/ad3dcb
摘要
Abstract This paper addresses the issue of nonfragile state estimation (SE) for memristive recurrent neural networks (MRNNs) 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. In this paper, a dynamic event-triggered mechanism (DETM) is employed to select useful data instead of a static event-triggered mechanism. By constructing a meaningful LyapunovKrasovskii functional (LKF), a delay-dependent criterion is derived in terms of linear matrix inequalities (LMIs) 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.
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