The exponential stability of high-order delayed BAM neural networks with Markovian jumping parameters

This paper concerns with the global exponential stability for a class of high-order BAM neural networks with mixed time-varying delays and Markovian jumping parameters. By constructing a novel Lyapunov-Krasovskii functional, and applying Linear Matrix Inequality (LMI) approach as well as stochastic analysis theory, a new sufficient condition is derived to guarantee the exponential stability in the mean square of the equilibrium point. The obtained result is easy to check and improves some existing stability results. A numerical example is provided to show the effectiveness of the theoretical result.