Global Exponential Stability of Impulsive Complex-Valued Neural Networks with Proportional Delays

Abstract

In this thesis, stability for a class of impulsive complex-valued neural networks with proportional time delay is discussed. By adhibiting an advisable vector Lyapunov function, making use of inequality craftsmanship and M-matrix theory, a sufficient condition is educed to insure the global exponential stability of the considered neural networks.