A graph-theoretic approach to boundedness of stochastic Cohen-Grossberg neural networks with Markovian switching

In this paper, a novel class of stochastic Cohen-Grossberg neural networks with Markovian switching (SCGNNMSs) is investigated, where the white noise and the color noise are taken into account. By utilizing Lyapunov method, some graph theory and M-matrix technique, several sufficient conditions are obtained to ensure the asymptotic boundedness of the SCGNNMSs. These criteria have a close relation to the topology property of the network and are easy to be verified in practice. Two numerical examples are also presented to substantiate the theoretical results.