A Global Robust Stability Criterion for Jumping Stochastic Cohen- Grossberg Neural Networks with Mode-Dependent Mixed Delays

The global robust stability problem is considered for a class of uncertain stochastic Cohen-Grossberg neural networks with Markovian jumping parameters and time-delay in this paper. The time delays are mode-dependent mixed delays including discrete delays and distributed delays. The jumping parameters considered here are generated from a continuous-time discretestate homogenous Markov chain, which are governed by a Markov process with discrete and finite state space. Based on the Lyapunov method and stochastic analysis approaches, a stability criterion is established, which can be expressed in terms of linear matrix inequalities (LMIS). Finally, a numerical example is given to demonstrate the effectiveness of the proposed results.