Exponential stability of stochastic interval cellular neural networks with delays

In this paper, the exponential stability problem of a class of stochastic interval delayed cellular neural networks is studied. Firstly, a kind of equivalent description of this stochastic interval delayed cellular neural networks is presented. Then by using the Ito formula, Razumikhin theorems, Lya...

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Bibliographic Details
Main Authors: Jin-Fang Han, Fa-Chao Li
Format: Conference Proceeding
Language:English
Subjects:
Cellular networks
Cellular neural networks
Delays
Exponential Stability
Indium tin oxide
Itô formula
Lyapunov function
Lyapunov method
Neural networks
Robust stability
Stability criteria
Stochastic Cellular Neural Networks
Stochastic processes
Stochastic systems
Sufficient conditions
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Summary:In this paper, the exponential stability problem of a class of stochastic interval delayed cellular neural networks is studied. Firstly, a kind of equivalent description of this stochastic interval delayed cellular neural networks is presented. Then by using the Ito formula, Razumikhin theorems, Lyapunov function and norm inequalities, several simple sufficient conditions are obtained which guarantee the exponential stability of the stochastic interval cellular neural networks, and some recent results reported in the literature are generalized.
ISSN:2158-5695
DOI:10.1109/ICWAPR.2009.5207427