pth moment exponential stability of memristor Cohen-Grossberg BAM neural networks with time-varying delays and reaction–diffusion

Author(s):  
Bandana Priya ◽  
M. Syed Ali ◽  
Ganesh Kumar Thakur ◽  
Sumaya Sanober ◽  
Bhawna Dhupia
2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Xiaoai Li ◽  
Jiezhong Zou ◽  
Enwen Zhu

This paper investigates the problem ofpth moment exponential stability for a class of stochastic neural networks with time-varying delays and distributed delays under nonlinear impulsive perturbations. By means of Lyapunov functionals, stochastic analysis and differential inequality technique, criteria onpth moment exponential stability of this model are derived. The results of this paper are completely new and complement and improve some of the previously known results (Stamova and Ilarionov (2010), Zhang et al. (2005), Li (2010), Ahmed and Stamova (2008), Huang et al. (2008), Huang et al. (2008), and Stamova (2009)). An example is employed to illustrate our feasible results.


2007 ◽  
Vol 26 (3) ◽  
pp. 191-200 ◽  
Author(s):  
Enwen Zhu ◽  
Haomin Zhang ◽  
Yong Wang ◽  
Jiezhong Zou ◽  
Zheng Yu ◽  
...  

2009 ◽  
Vol 2009 ◽  
pp. 1-18 ◽  
Author(s):  
Chuangxia Huang ◽  
Xinsong Yang ◽  
Yigang He

This paper is concerned withpth moment exponential stability of stochastic reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. With the help of Lyapunov method, stochastic analysis, and inequality techniques, a set of new suffcient conditions onpth moment exponential stability for the considered system is presented. The proposed results generalized and improved some earlier publications.


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