Robust stability of uncertain neural networks with time-varying delays

Abstract

This paper is concerned the robust stability analysis problem for neural networks with time-varying delay and time-varying parametric uncertainties. By utilizing a Lyapunov-Krasovskii functional, we show that the addressed neural networks are robustly, asymptotically stable if a convex optimization problem is feasible. A stability criterion is derived and formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Two numerical examples are given to demonstrate the usefulness of the proposed robust stability criterion. © 2008 Springer-Verlag Berlin Heidelberg.