The pth Moment Exponential Synchronization of Drive-Response Memristor Neural Networks Subject to Stochastic Perturbations

被引：0
|
作者：
Wang, Xiaobo [1 ,2 ]
Wu, Xuefei [3 ]
Nie, Zhe [4 ]
Yan, Zengxian [5 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management, Nanjing 211106, Jiangsu, Peoples R China
[2] Nanjing Xiaozhuang Univ, Business Sch, Nanjing 211171, Peoples R China
[3] Shenzhen Polytech, Artificial Intelligence Sch, Shenzhen 518055, Peoples R China
[4] Shenzhen Polytech, Digital Creat & Animat Sch, Shenzhen 518055, Peoples R China
[5] Guangxi Modern Polytech Coll, Hechi 547000, Guangxi, Peoples R China
来源：
COMPLEXITY | 2023年 / 2023卷
关键词：
OSCILLATORS; CONSENSUS;
D O I：
10.1155/2023/1335184
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, the pth moment exponential synchronization problems of drive-response stochastic memristor neural networks are studied via a state feedback controller. The dynamics of the memristor neural network are nonidentical, consisting of both asymmetrically nondelayed and delayed coupled, state-dependent, and subject to exogenous stochastic perturbations. The pth moment exponential synchronization of these drive-response stochastic memristor neural networks is guaranteed under some testable and computable sufficient conditions utilizing differential inclusion theory and Filippov regularization. Finally, the correctness and effectiveness of our theoretical results are demonstrated through a numerical example.
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页数：10
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