Exponential Stability for Delayed Neural Networks using Extended Reciprocally Convex Matrix Inequality

In this paper, the problem of the exponential stability for delayed neural networks with a time-varying delay is investigated. Firstly, a novel Lyapunov-Krasovskii functional with simple augmented terms is constructed. Then, a less conservative stability criterion is obtained via a newly developed free-matrix-based integral inequality and an extended reciprocally convex matrix inequality. Meanwhile, the complexity of the techniques used is in consideration. Finally, two numerical examples are given to illustrate the effectiveness of the proposed results.