Output Synchronization in Coupled Neural Networks With and Without External Disturbances

被引：72
作者：
Wang, Jin-Liang [1 ,2 ]
Wu, Huai-Ning [3 ]
Huang, Tingwen [4 ]
Xu, Meng [1 ]
机构：
[1] Tianjin Polytech Univ, Sch Comp Sci & Software Engn, Tianjin 300387, Peoples R China
[2] Tianjin Polytech Univ, Tianjin Key Lab Optoelect Detect Technol & Syst, Tianjin 300387, Peoples R China
[3] Beihang Univ, Sch Automat Sci & Elect Engn, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China
[4] Texas A&M Univ, Doha 23874, Qatar
来源：
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS | 2018年 / 5卷 / 04期
基金：
中国国家自然科学基金;
关键词：
H-infinity output synchronization; coupled neural networks (CNNs); coupling weights; output synchronization; SAMPLED-DATA SYNCHRONIZATION; COMPLEX DYNAMICAL NETWORKS; H-INFINITY SYNCHRONIZATION; EXPONENTIAL SYNCHRONIZATION; ROBUST SYNCHRONIZATION; ARRAY; STABILIZATION; STABILITY; DELAYS;
D O I：
10.1109/TCNS.2017.2782488
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper studies the output synchronization of coupled neural networks (CNNs) as well as the effects of external disturbances. By employing matrix theory and Barbalat's Lemma, several output synchronization criteria are presented for CNNs with directed and undirected topologies, respectively. Moreover, in order to ensure the output synchronization of CNNs, two adaptive schemes to adjust the coupling weights are designed. On the other hand, we, respectively, analyze the H-infinity output synchronization of directed and undirected CNNs with external disturbances, and two adaptive strategies for updating the coupling weights are designed to guarantee the H-infinity output synchronization of CNNs. Finally, two examples of CNNs are also given to verify the proposed output synchronization criteria.
引用
收藏
页码：2049 / 2061
页数：13
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