Resilient H∞ filtering for discrete-time uncertain Markov jump neural networks over a finite-time interval

In this paper, the resilient finite-time H∞ filtering problem for discrete-time uncertain Markov jump neural networks with packet dropouts is investigated. The purpose is to design a filter which is insensitive with respect to filter gain uncertainties subjects to an H∞ performance level. The data packet dropouts phenomenon modeled by a stochastic Bernoulli distributed process is also considered. In terms of the linear matrix inequalities methodology, some sufficient conditions which guarantee that the filtering error system is finite-time bounded with a prescribed H∞ performance level are established. Based on the conditions, an explicit expression for the desired filter is given. A numerical example is provided to illustrate the validness of the proposed scheme.