Quantized state estimation for neural networks with cyber attacks and hybrid triggered communication scheme

This paper is concerned with the issue of quantized state estimation for neural networks with cyber attacks and hybrid triggered communication scheme. In order to reduce the pressure of the network transmission and save the network resources, the hybrid triggered scheme and quantization are introduced. The hybrid triggered scheme consists of time triggered scheme and event triggered scheme, in which the stochastic switch is described by a variable satisfying Bernoulli distribution. First, by taking the effect of hybrid triggered scheme and quantization into consideration, a mathematical model for estimating the state of neural networks is constructed. Second, by using linear matrix inequality (LMI) techniques and Lyapunov stability theory, the sufficient conditions are given which can ensure the stability of estimating error system under hybrid triggered scheme, and the designing algorithm of desired state estimator is also presented in terms of LMIs. Finally, a numerical example is given to show the usefulness of the proposed approach.