Adaptive event-triggered extended dissipative synchronization of delayed reaction-diffusion neural networks under deception attacks

被引：20
作者：
Zhao, Feng-Liang [1 ]
Wang, Zi-Peng [2 ]
Qiao, Junfei [2 ]
Wu, Huai-Ning [3 ]
Huang, Tingwen [4 ]
机构：
[1] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510006, Peoples R China
[2] Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
[3] Beihang Univ, Sch Automat Sci & Elect Engn, Sci & Technol Aircraft Control Lab, Beijing 1001911, Peoples R China
[4] Texas A&M Univ, Sci Program, College Stn, TX USA
来源：
NEURAL NETWORKS | 2023年 / 166卷
基金：
中国博士后科学基金;
关键词：
Adaptive event-triggered sampled-data; (AETSD) control; Delayed reaction-diffusion neural; networks (RDNNs); Extended dissipative performance; Spatially averaged measurements (SAMs); Deception attacks; SYSTEMS; STABILITY;
D O I：
10.1016/j.neunet.2023.07.024
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Under spatially averaged measurements (SAMs) and deception attacks, this article mainly studies the problem of extended dissipativity output synchronization of delayed reaction-diffusion neural networks via an adaptive event-triggered sampled-data (AETSD) control strategy. Compared with the existing ETSD control methods with constant thresholds, our scheme can be adaptively adjusted according to the current sampling and latest transmitted signals and is realized based on limited sensors and actuators. Firstly, an AETSD control scheme is proposed to save the limited transmission channel. Secondly, some synchronization criteria under SAMs and deception attacks are established by utilizing Lyapunov-Krasovskii functional and inequality techniques. Then, by solving linear matrix inequalities (LMIs), we obtain the desired AETSD controller, which can satisfy the specified level of extended dissipativity behaviors. Lastly, one numerical example is given to demonstrate the validity of the proposed method.& COPY; 2023 Elsevier Ltd. All rights reserved.
引用
收藏
页码：366 / 378
页数：13
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