State estimation for complex-valued memristive neural networks with time-varying delays

Abstract

This paper focuses on the state estimation problem for complex-valued memristive neural networks with time-varying delays. By utilizing Lyapunov stability theory and some matrix inequality techniques, based on a novel Lyapunov functional, a sufficient delay-dependent condition which guarantees that the error-state system is global asymptotically stable is firstly derived for the addressed system, and a suitable state estimator is also designed. Finally, an example is given to illustrate the present method.