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. HighlightsThe main contribution lies in some sufficient conditions provided to guarantee the filtering error system is finite-time bounded with a prescribed performance level.A resilient filter has been designed which can provide safe tuning margins and tolerate uncertainties in their coefficients. Therefore, the designed filter is more general than some existing ones.The packet dropouts phenomenon described by a stochastic Bernoulli distributed process is also considered which makes the desired filter structure more common.