Mean-square exponential stability of stochastic Hopfield neural networks with time-varying discrete and distributed delays

摘要

In this Letter, the mean-square exponential stability problem for stochastic Hopfield neural networks with both discrete and distributed time-varying delays is investigated. By choosing a modified Lyapunov-Krasovskii functional, a delay-dependent criterion is established such that the stochastic neural network is mean-square exponentially stable. The derivative of discrete time-varying delay h(t) satisfies (h) over dot(t) <= eta and the decay rate beta can be any finite positive value without any other constraints. The assumptions given in this Letter are more general than the conventional assumptions (i.e., (h) over dot(t) <= eta < 1 and beta satisfies a transcendental equation or an inequality). Finally, numerical examples are provided to illustrate the effectiveness of the proposed sufficient conditions.