Global exponential stability of reaction–diffusion recurrent neural networks with time-varying delays

2003 ◽  
Vol 314 (5-6) ◽  
pp. 434-442 ◽  
Author(s):  
Jinling Liang ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Recurrent Neural Networks ◽  
Global Exponential Stability ◽  
Reaction Diffusion ◽  
Time Varying

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.

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