Non-fragile state estimation for discrete-time neural network system with randomly occurring sensor saturations

This paper investigates the problem for the non-fragile state estimation of discrete-time neural network system with randomly occurring sensor saturations and time delays. In order to show the possible gain variations occurring in complex environments, a non-fragile state estimator is designed to ensure the estimation error converges to zero exponentially. And, by using a sensor saturation function to deals with the sensor saturation phenomenon. Then, Lyapunnov-Krasovskii functional approach is proposed, sufficient conditions are established to guarantee the existence of the desired state estimator. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.