非周期图
控制理论(社会学)
服务拒绝攻击
计算机科学
控制器(灌溉)
李雅普诺夫函数
人工神经网络
干扰
线性矩阵不等式
马尔可夫过程
理论(学习稳定性)
事件(粒子物理)
数学
数学优化
控制(管理)
人工智能
非线性系统
统计
物理
互联网
组合数学
量子力学
机器学习
万维网
农学
生物
热力学
作者
Shanshan Zhao,Haiyang Zhang,Lianglin Xiong,Huizhen Chen
出处
期刊:
日期:2022-11-25
卷期号:: 6329-6334
标识
DOI:10.1109/cac57257.2022.10055059
摘要
In this paper, the asymptotic stabilization of Markov Jump Neural Networks (MJNNs) with time-varying delays under aperiodic Denial-of-Service (DoS) attacks is studied. Firstly, in order to lessen the "unnecessary" waste of network resources, a Resilient Event-triggered Communication Scheme (RETCS) is designed for aperiodic DoS attacks, and then a new MJNNs model with time-varying delay considering aperiodic DoS attacks is established. Secondly, a new Lyapunov function is constructed. Based on Lyapunov stability theory and linear matrix inequality technique, the stability criterion of MJNNs with time-varying delay is obtained. Then, the criteria for cooperative design of the trigger parameters of RETCS and the gain matrix of the controller is proposed. A numerical example is then provided to demonstrate the validity of the findings.
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