Robust stability of Markovian Jump neural networks with mixed delays

In this paper, the problem of robust stability for a class of neural networks with Markovian jump parameters and mixed time-delays is investigated. The jump parameters are modeled as a continuous-time, discrete-state Markov process and the mixed delays comprise discrete and distributed time-delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, some robust stability conditions for the Markovian jump neural networks with mixed delays are derived. The proposed LMI-based criteria are computationally efficient and they can be solved readily with recently developed numerical packages. An example is given to show the effectiveness of the obtained results.