Delay-range-dependent stability for static recurrent neural networks with time-varying delays

This paper studies the asymptotic stability analysis for static recurrent neural networks with interval time-varying delays. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. Without introducing any free-weighting matrices, some delay-range-dependent stability criteria are established by constructing a new Lyapunov-Krasovskii functional. As a result, the criteria involve less variables and have low computational complexity. An example is given to show the effectiveness and the benefits of the proposed method.