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In this paper, the exponential robust stability for stochastic interval Hopfield neural networks with time-varying delays is investigated. Based on Lyapunov functional approach and linear matrix inequality (LMI) technique, the sufficient conditions are proposed to ensure stochastic interval Hopfield neural networks to be exponential robustly stable.
Using Chebyshev inequality and nonnegative semi-martingale convergence theorem, the paper investigates asymptotic behavior of stochastic Cohen-Grossberg neural networks with delay by constructing suitable Lyapunov functional. Algebraic criteria are given for stochastic ultimate bounded and almost exponential stability. The result in the paper extend the main conclusion. In the end, examples are given...
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