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 (NN) 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 dissipafivity 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. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.