Improved robust stability criteria for neural networks with fast time-varying delays

Abstract

An improved robust global stability criterion is developed for uncertain neural networks with fast time-varying delays. The networks have norm-bounded parametric uncertainties. The relationship between the time-varying delay and associated extreme bounds (lower and upper) is appropriately exploited when dealing with the Lyapunov functional derivative. The developed stability criterion is delay dependent and is characterized by linear-matrix-inequality-based conditions. Numerical examples are presented to illustrate the benefits and lower conservativeness of the developed method.