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.