Global Exponential Stability of Fuzzy Cellular Neural Networks with Mixed Delays

2008 ◽  
Author(s):  
Ranchao Wu ◽  
Liping Chen
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Cellular Neural Networks ◽  
Mixed Delays ◽  
Fuzzy Cellular Neural Networks

GLOBAL EXPONENTIAL STABILITY OF IMPULSIVE FUZZY CELLULAR NEURAL NETWORKS WITH DELAYS AND DIFFUSION

2009 ◽  
Vol 19 (01) ◽  
pp. 245-261 ◽  
Author(s):  
KELIN LI
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Matrix Theory ◽  
Sufficient Conditions ◽  
Cellular Neural Networks ◽  
Mixed Delays ◽  
Fuzzy Cellular Neural Networks ◽  
Inequality Technique ◽  
And Diffusion

In this paper, a class of impulsive fuzzy cellular neural networks (FCNNs) with mixed delays and diffusion is formulated and investigated. By establishing an intergro-differential inequality, applying M-matrix theory and inequality technique, several sufficient conditions are obtained to ensure the existence, uniqueness and global exponential stability of an equilibrium point for impulsive FCNNs with mixed delays and diffusion. In particular, the estimate of the exponential convergence rate is also provided, which depends on the system parameters and impulses. These results generalize and improve the earlier publications. Some examples are given to show the effectiveness of the obtained results. It is believed that these results are significant and useful for the design and applications of FCNNs.

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