This paper presents sufficient conditions for asymptotic and exponential stability of a class of artificial neural networks (ANNs) subject to constant or time-varying delay and polytope-bounded uncertainties. The proposed approach is based on Lyapunov-Krasovskii stability theory, and the linear matrix inequalities (LMIs) technique introducing slack matrices so that convex optimization algorithms can be used. Three examples with numerical simulations are performed to demonstrate the effectiveness of the proposed method. The first example deals with the asymptotic stability analysis, the second one with the robust stability analysis and the last one with exponential stability.
Lyapunov-Krasovskii Theory; Robust Asymptotic and Exponential Stability; Linear Matrix Inequality; Neural Networks; Time-Delay