Stochastic finite-time state estimation for discrete time-delay neural networks with Markovian jumps

This paper investigates the problem of stochastic finite-time state estimation for a class of uncertain discrete-time Markovian jump neural networks with time-varying delays. A state estimator is designed to estimate the network states through available output measurements such that the resulted error dynamics is stochastically finite-time stable. By stochastic Lyapunov-Krasovskii functional approach, sufficient conditions are derived for the error dynamics to be stochastic finite-time stable. The desired state estimator is designed via linear matrix inequality technique. Simulation examples are provided to illustrate the effectiveness of the obtained results. (C) 2014 Elsevier B.V. All rights reserved.