Stability analysis of inertial Cohen-Grossberg neural networks with Markovian jumping parameters

In this paper, we concentrate on the problem of global asymptotical stability for a class of Markovian jump inertial Cohen-Grossberg neural networks. The jumping parameters are described with a continuous-time, finite-state Markov chain. By adopting the method of model transformation, differential mean value theorem, Lyapunov stability theory and linear matrix inequality techniques, we derive some novel sufficient conditions to guarantee the global asymptotical stability for the addressed systems. It is worth mentioning that the model investigated in this letter comprises and generalizes many existing results in the previous literature. Finally, the effectiveness of the theoretical results is validated by numerical examples.