Stability of stochastic neural networks with Markovian jumping parameters

The global asymptotical stability for a class of stochastic delayed neural networks (SDNNs) with Markovian jumping parameters is considered. By applying Lyapunov functional method and Ito's differential rule, new delay-dependent stability conditions are derived. All results are expressed in terms of linear matrix inequality (LMI), and a numerical example is presented to illustrate the correctness and less conservativeness of the proposed method.