Asynchronous H∞H∞ filtering for discrete-time Markov jump neural networks

This paper is concerned with the asynchronous H∞H∞ filtering problem for discrete-time Markov jump neural networks. The asynchronous phenomenon is considered and two different Markov chains are used to govern the jump mode of the filter and that of the neural networks respectively, which means that their modes need not be corresponding to each other. A novel filtering design method is proposed. By introducing a unified Lyapunov functional, a sufficient condition is derived in terms of linear matrix inequality (LMI) such that the resultant filtering error system is stochastically stable. A numerical example is given to demonstrate the effectiveness of the proposed theoretical results.