scholarly journals Stability Analysis of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Time-Varying Delays

2009 ◽  
Vol 2009 ◽  
pp. 1-18 ◽  
Author(s):  
Chuangxia Huang ◽  
Xinsong Yang ◽  
Yigang He
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Stochastic Analysis ◽  
Lyapunov Method ◽  
Reaction Diffusion ◽  
Time Varying ◽  
Inequality Techniques ◽  
Stochastic Reaction ◽  
Moment Exponential Stability

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.

scholarly journals Stability of the Stochastic Reaction-Diffusion Neural Network with Time-Varying Delays andp-Laplacian

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Pan Qingfei ◽  
Zhang Zifang ◽  
Huang Jingchang
Keyword(s):  
Neural Network ◽  
Exponential Stability ◽  
Reaction Diffusion ◽  
Time Varying ◽  
The Neural Network ◽  
The Moment ◽  
Delay Differential ◽  
Stochastic Reaction ◽  
Moment Exponential Stability ◽  
Delay Differential Inequality

The main aim of this paper is to discuss moment exponential stability for a stochastic reaction-diffusion neural network with time-varying delays andp-Laplacian. Using the Itô formula, a delay differential inequality and the characteristics of the neural network, the algebraic conditions for the moment exponential stability of the nonconstant equilibrium solution are derived. An example is also given for illustration.

scholarly journals Pth Moment Exponential Stability of Impulsive Stochastic Neural Networks with Mixed Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Xiaoai Li ◽  
Jiezhong Zou ◽  
Enwen Zhu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Stochastic Analysis ◽  
Differential Inequality ◽  
Stochastic Neural Networks ◽  
Mixed Delays ◽  
Time Varying ◽  
Inequality Technique ◽  
Pth Moment Exponential Stability ◽  
Moment Exponential Stability

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.

MEAN VALUE EXPONENTIAL STABILITY OF STOCHASTIC REACTION–DIFFUSION GENERALIZED COHEN–GROSSBERG NEURAL NETWORKS WITH TIME-VARYING DELAY

2007 ◽  
Vol 17 (09) ◽  
pp. 3219-3227 ◽  
Author(s):  
LI WAN ◽  
QINGHUA ZHOU ◽  
JIANHUA SUN
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Mean Value ◽  
Time Varying ◽  
Time Varying Delay ◽  
The Stability ◽  
Stochastic Reaction ◽  
Varying Delay

Stochastic effects on the stability property of reaction–diffusion generalized Cohen–Grossberg neural networks (GDCGNNs) with time-varying delay are considered. By skillfully constructing suitable Lyapunov functionals and employing the method of variational parameters, inequality technique and stochastic analysis, the delay independent and easily verifiable sufficient conditions to guarantee the mean-value exponential stability of an equilibrium solution associated with temporally uniform external inputs to the networks are obtained. One example is given to illustrate the theoretical results.

scholarly journals Global Exponential Stability and Periodicity of Nonautonomous Impulsive Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weiyi Hu ◽  
Kelin Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Fixed Point Theory ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Point Theory ◽  
Time Varying ◽  
Nonautonomous Systems ◽  
Inequality Techniques

In this paper, we investigate the global exponential stability and periodicity of nonautonomous cellular neural networks with reaction-diffusion, impulses, and time-varying delays. By establishing a new differential inequality for nonautonomous systems, using the properties of M-matrix and inequality techniques, some new sufficient conditions for the global exponential stability of the system are obtained. Moreover, sufficient conditions for the periodic solutions of the system are obtained by using the Poincare mapping and the fixed point theory. The validity and superiority of the main results are verified by numerical examples and simulations.

EXPONENTIAL p-STABILITY OF STOCHASTIC REACTION–DIFFUSION CELLULAR NEURAL NETWORKS WITH MULTIPLE DELAYS

2009 ◽  
Vol 19 (10) ◽  
pp. 3373-3386 ◽  
Author(s):  
RANCHAO WU
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Cellular Neural Networks ◽  
Matrix Analysis ◽  
Multiple Delays ◽  
Space Matrix ◽  
Stochastic Reaction ◽  
Moment Exponential Stability

In the current paper, a class of stochastic cellular neural networks with reaction–diffusion effects, both discrete and distributed time delays, is studied. Several sufficient conditions guaranteeing the almost sure and pth moment exponential stability of its equilibrium solution are respectively obtained through analytic methods such as employing Lyapunov functional, applying Itô's formula, inequality techniques, embedding in Banach space, Matrix analysis and semimartingale convergence theorem. The yielded conclusions, which are independent of diffusion terms and delays, assume much less restrictions on activation functions and interconnection weights, and can be applied within a broader range of neural networks. Moreover, through the obtained results, it could be noted that noise will affect the exponential stability of the system. For illustration, two examples are given to show the feasibility of results.

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