Mean-Square Exponential Stability of Stochastic Interval Cellular Neural Networks with Time-Varying Delay

Abstract:

This paper is concerned with the mean-square exponential stability analysis problem for a class of stochastic interval cellular neural networks with time-varying delay. By using the stochastic analysis approach, employing Lyapunov function and norm inequalities, several mean-square exponential stability criteria are established in terms of the formula and Razumikhin theorem to guarantee the stochastic interval delayed cellular neural networks to be mean-square exponential stable. Some recent results reported in the literatures are generalized. A kind of equivalent description for this stochastic interval cellular neural networks with time-varying delay is also given.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1742-1747
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1742
• Online since: September 2013
• Authors: Jin Fang Han
• Keywords: Exponential Stability, Ito Formula, Lyapunov Function, Stochastic Cellular Neural Networks, Time-Varying Delay

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