Stability and dissipativity analysis of static neural networks with interval time-varying delay

This paper focuses on the problems of stability and dissipativity analysis for static neural networks (NNs) with interval time-varying delay. A new augmented Lyapunov-Krasovskii functional is firstly constructed, in which the information on the activation function is taken fully into account. Then, by employing a Wirtinger-based inequality to estimate the derivative of Lyapunov-Krasovskii functional, an improved stability criterion is derived for the considered neural networks. The result is extended to dissipativity analysis and a sufficient condition is established to assure the neural networks strictly dissipative. Two numerical examples are provided to demonstrate the effectiveness and the advantages of the proposed method.