Global Exponential Stability of Recurrent Neural Networks with Infinite Time-Varying Delays and Reaction-Diffusion Terms

2005 ◽  
pp. 132-141
Author(s):  
Qiankun Song ◽  
Zhenjiang Zhao ◽  
Xuedong Chen
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Recurrent Neural Networks ◽  
Global Exponential Stability ◽  
Reaction Diffusion ◽  
Time Varying ◽  
Infinite Time

scholarly journals Existence and Global Exponential Stability of Equilibrium Solution to Reaction-Diffusion Recurrent Neural Networks on Time Scales

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Kaihong Zhao ◽  
Yongkun Li
Keyword(s):  
Neural Networks ◽  
Boundary Conditions ◽  
Exponential Stability ◽  
Time Scales ◽  
Recurrent Neural Networks ◽  
Global Exponential Stability ◽  
Equilibrium Solution ◽  
Dirichlet Boundary ◽  
Reaction Diffusion ◽  
Dirichlet Boundary Conditions

The existence of equilibrium solutions to reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales is proved by the topological degree theory and M-matrix method. Under some sufficient conditions, we obtain the uniqueness and global exponential stability of equilibrium solution to reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales by constructing suitable Lyapunov functional and inequality skills. One example is given to illustrate the effectiveness of our results.

Sign in / Sign up

Export Citation Format

Share Document