文摘
In this paper, the passivity problem is researched for discrete-time stochastic neural networks with time-varying delay. By utilizing a novel Lyapunov–Krasovskii functional, delay-decomposition method and reciprocally convex approach, some sufficient delay-dependent passivity conditions are established in the form of linear matrix inequalities. Furthermore, these new criteria do not require all the symmetric matrices involved in the employed Lyapunov–Krasovskii functional to be positive definite. Finally, three numerical examples are given to illustrate the reduced conservatism and effectiveness of the proposed method.