Exponential Stability and Synchronization Control of Neural Networks

2015 ◽  
pp. 13-36
Author(s):  
Wuneng Zhou ◽  
Jun Yang ◽  
Liuwei Zhou ◽  
Dongbing Tong
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Synchronization Control

scholarly journals Inequalities and pth moment exponential stability of impulsive delayed Hopfield neural networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yutian Zhang ◽  
Guici Chen ◽  
Qi Luo
Keyword(s):  
Neural Networks ◽  
Lyapunov Function ◽  
Exponential Stability ◽  
Hopfield Neural Networks ◽  
New Method ◽  
Integral Inequalities ◽  
Delay Function ◽  
Pth Moment Exponential Stability ◽  
Moment Exponential Stability

AbstractIn this paper, the pth moment exponential stability for a class of impulsive delayed Hopfield neural networks is investigated. Some concise algebraic criteria are provided by a new method concerned with impulsive integral inequalities. Our discussion neither requires a complicated Lyapunov function nor the differentiability of the delay function. In addition, we also summarize a new result on the exponential stability of a class of impulsive integral inequalities. Finally, one example is given to illustrate the effectiveness of the obtained results.

scholarly journals Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays

2011 ◽  
Vol 21 (4) ◽  
pp. 649-658 ◽  
Author(s):  
Qianhong Zhang ◽  
Lihui Yang ◽  
Daixi Liao
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Exponential Stability ◽  
Differential Inequality ◽  
Sufficient Conditions ◽  
Coincidence Degree ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Fuzzy Cellular Neural Networks ◽  
Inequality Technique

Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays Fuzzy cellular neural networks with time-varying delays are considered. Some sufficient conditions for the existence and exponential stability of periodic solutions are obtained by using the continuation theorem based on the coincidence degree and the differential inequality technique. The sufficient conditions are easy to use in pattern recognition and automatic control. Finally, an example is given to show the feasibility and effectiveness of our methods.

Finite-time synchronization control of a class of memristor-based recurrent neural networks

2015 ◽  
Vol 63 ◽  
pp. 133-140 ◽  
Author(s):  
Minghui Jiang ◽  
Shuangtao Wang ◽  
Jun Mei ◽  
Yanjun Shen
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Synchronization Control

scholarly journals Global exponential stability of delayed inertial competitive neural networks

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Min Shi ◽  
Juan Guo ◽  
Xianwen Fang ◽  
Chuangxia Huang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Global Exponential Stability
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