Global Exponential Synchronization of Two Memristor-Based Recurrent Neural Networks With Time Delays via Static or Dynamic Coupling

2015 ◽  
Vol 45 (2) ◽  
pp. 235-249 ◽  
Author(s):  
Zhenyuan Guo ◽  
Jun Wang ◽  
Zheng Yan
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Time Delays ◽  
Exponential Synchronization ◽  
Dynamic Coupling ◽  
Global Exponential Synchronization

Exponential synchronization of memristor-based recurrent neural networks with time delays

2011 ◽  
Vol 74 (17) ◽  
pp. 3043-3050 ◽  
Author(s):  
Ailong Wu ◽  
Zhigang Zeng ◽  
Xusheng Zhu ◽  
Jine Zhang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Time Delays ◽  
Exponential Synchronization

scholarly journals Exponential Synchronization in Inertial Neural Networks with Time Delays

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 356 ◽  
Author(s):  
Liang Ke ◽  
Wanli Li
Keyword(s):  
Differential Equation ◽  
Neural Networks ◽  
Lyapunov Functional ◽  
Time Delays ◽  
Order Differential Equation ◽  
Sufficient Conditions ◽  
Exponential Synchronization ◽  
Inertial Neural Networks ◽  
Response Systems ◽  
Global Exponential Synchronization

In this paper, exponential synchronization for inertial neural networks with time delays is investigated. First, by introducing a directive Lyapunov functional, a sufficient condition is derived to ascertain the global exponential synchronization of the drive and response systems based on feedback control. Second, by introducing a variable substitution, the second-order differential equation is transformed into a first-order differential equation. As such, a new Lyapunov functional is constructed to formulate a novel global exponential synchronization for the systems under study. The two obtained sufficient conditions complement each other and are suitable to be applied in different cases. Finally, two numerical examples are given to illustrated the effectiveness of the proposed theoretical results.

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