New global exponential stability criterion for neural networks with time-varying delay

This paper is concerned with global exponential stability problem for a class of neural networks with time-varying delays. An appropriate Lyaponov-krasovskii functional (LKF) is constructed firstly. The symmetric matrices involved in the LKF are not required to be all positive definite. The obtained criterion expressed in terms of linear matrix inequalities (LMIs) shows advantages over the existing ones since not only a novel LKF is constructed but also several techniques such as integral inequalities, convex combination technique, reciprocally convex technique and free matrix method are used to estimate the upper bound of the derivative of the LKF. Finally, two numerical examples are given to show the effectiveness and superiority of the obtained criterion.