State estimation for memristor-based neural networks with time-varying delays

摘要

This paper is concerned with the state estimation problem for a class of memristor-based neural networks with time-varying delay. A delay dependent condition is developed to estimate the neuron states through observed output measurements such that the error system is globally asymptotically stable. By constructing more effective Lyapunov functionals, and combining with Jensen integral inequality and free-weighting matrix approach, a less conservative sufficient condition for the existence of state estimator is formulated in terms of linear matrix inequality, which can be checked efficiently by using some standard numerical packages. Finally, a numerical example is given to demonstrate the effectiveness of the presented results.