Dissipativity-based state estimation for Markov jump discrete-time neural networks with unreliable communication links

This paper investigates the problem of dissipativity-based state estimator design for Markov jump discrete-time neural networks, where the communication links between the neural network and estimator are assumed to be imperfect. The phenomenon of missing data is modeled by a stochastic variable following the Bernoulli random distribution. The focus is on the design of a Markov switching estimator such that the resulting closed-loop system is dissipative. Some sufficient conditions for the existence of admissible estimator are obtained in terms of linear matrix inequalities. Finally, a numerical example is employed to demonstrate the effectiveness of our proposed approach.