Global Exponential Stability of Fuzzy Cohen-Grossberg Neural Networks with Variable Delays
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Global Asymptotic Stability of Fuzzy Cellular Neural Networks with Unbounded Distributed Delays
Neural Processing Letters
Robust stability of delayed fuzzy Cohen-Grossberg neural networks
Computers & Mathematics with Applications
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Impulsive Effects on Stability of Fuzzy Cohen–Grossberg Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, a class of stochastic fuzzy Cohen-Grossberg neural networks with reaction-diffusion and distributed delays is discussed. Based on Lyapunov functionals, inequality analysis, Ito's formula, nonnegative semimartingale convergence theorem and stochastic analysis, some stability sufficient conditions are presented to guarantee the neural networks to be almost sure and mean square exponentially stable, respectively. The proposed results improve and extend some earlier literature and are easier to verify. For illustration, an example is given to illustrate the feasibility of the results.