Stability Analysis for Delayed Neural Networks via a Relaxed Negative-Determination Quadratic Function Method

This paper presents an improved stability condition for neural networks with a time-varying delay. First, an improved Lyapunov-Krasovskii functional (LKF) is constructed by introducing the delay-product term and the augmented term. Then, a less conservative delay-dependent stability criterion for neural networks with a time-varying delay is established by utilizing the generalized reciprocally convex combination and a relaxed quadratic function negative-determination. Finally, a numerical example is used to illustrate the merit and effectiveness of the proposed stability criterion.