文献类型：期刊文献

英文题名：Secure Communication Based on Quantized Synchronization of Chaotic Neural Networks Under an Event-Triggered Strategy

作者：He, Wangli[1,2];Luo, Tinghui[1,2];Tang, Yang[1,2];Du, Wenli[1,2];Tian, Yu-Chu[3];Qian, Feng[1,2]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China;[3]Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4001, Australia

年份：2020

卷号：31

期号：9

起止页码：3334

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20203809203012);WOS:【SCI-EXPANDED(收录号:WOS:000566342500016)】；

基金：This work was supported in part by the Major Program of the Science and Technology Ministry in China under Grant 2018AAA0101600, in part by the National Natural Science Foundation of China under Grant 61922030 and Grant 61773163, in part by the Young Elite Scientists Sponsorship Program by CAST under Grant 2016QNRC001, in part by the Natural Science Foundation of Shanghai under Grant 17ZR1444600, in part by the Shanghai Rising-Star Program under Grant 18QA1401400, in part by the Fundamental Research Funds for the Central Universities under Grant 222201917006 and Grant 50321081916019, in part by the 111 Project of the Ministry of Education of China under Grant B17017, and in part by the Australian Research Council through the Discovery Project Scheme under Grant DP170103305.

语种：英文

外文关键词：Synchronization; Chaotic communication; Quantization (signal); Output feedback; Biological neural networks; Security; Chaotic neural networks; event-triggered strategy; quantized synchronization; secure communication

摘要：This article presents a secure communication scheme based on the quantized synchronization of master-slave neural networks under an event-triggered strategy. First, a dynamic event-triggered strategy is proposed based on a quantized output feedback, for which a quantized output feedback controller is formed. Second, theoretical criteria are derived to ensure the bounded synchronization of master-slave neural networks. With these criteria, an explicit upper bound is given for the synchronization error. Sufficient conditions are also provided on the existence of quantized output feedback controllers. A Chua's circuit is chosen to illustrate the effectiveness of our theoretical results. Third, a secure communication scheme is presented based on the synchronization of master-slave neural networks by combining the basic principle of cryptology. Then, a secure image communication is studied to verify the feasibility and security performance of the proposed secure communication scheme. The impact of the quantization level and the event-triggered control (ETC) on image decryption is investigated through experiments.