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Global Exponential Estimates of Stochastic Cohen-Grossberg Neural Networks with Time Delay

This paper is concerned with the exponential estimating problem for Cohen-Grossberg neural networks with time delay and stochastic disturbance. A sufficient condition, which does not only guarantee the global exponential stability but also provides more exact characterization on the decay rate and the coefficient, is established in terms of the Lyapunov-Krasovskii functional approach and the linear matrix inequality (LMI) technique. The estimates of the decay rate and the coefficient are obtained by solving a set of LMIs, which can be checked easily by effective algorithms. In addition, slack matrices are introduced to reduce the conservatism of the condition. A numerical example is provided to illustrate the effectiveness of the theoretical results.