Finite-time synchronization of memristive neural networks with parameter uncertainties via aperiodically intermittent adjustment

被引：17
|
作者：
Zhang, Shuai [2 ]
Yang, Yongqing [1 ]
Sui, Xin [2 ]
Xu, Xianyu [1 ]
机构：
[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Jiangsu, Peoples R China
[2] Jiangnan Univ, Sch IoT Engn, Wuxi 214122, Jiangsu, Peoples R China
来源：
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2019年 / 534卷
关键词：
Finite-time; Synchronization; Memristive neural network; Parameter uncertainties; Aperiodically intermittent; 2ND-ORDER MULTIAGENT SYSTEMS; GLOBAL EXPONENTIAL SYNCHRONIZATION; DELAYED NONLINEAR DYNAMICS; VARYING DELAYS; LIMIT-CYCLES; MIXED DELAYS; PINNING SYNCHRONIZATION; COMPLEX NETWORKS; STABILITY; BIFURCATION;
D O I：
10.1016/j.physa.2019.122258
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
This paper is concerned with finite-time synchronization of memristive neural networks with parameter uncertainties by using aperiodically intermittent strategy. Based on the existed finite-time theory of intermittent strategy, a finite-time aperiodically intermittent stability lemma is proposed. In the sense of Filippov's framework, an appropriate Lyapunov functional is constructed to realize the finite-time synchronization by designing a appropriate aperiodically intermittent controller. The derived sufficient conditions can effectively eliminate the influence of parameter uncertainties and make the systems achieve finite-time synchronization. Finally, a numerical simulation example is given to demonstrate the validity of the sufficient conditions. (C) 2019 Elsevier B.V. All rights reserved.
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页数：16
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