文摘
This paper studies the mean-square exponential input-to-state stability of delayed Cohen–Grossberg neural networks with Markovian switching. By using the vector Lyapunov function and property of to view the MathML source">M-matrix, two generalized Halanay inequalities are established. By means of the generalized Halanay inequalities, sufficient conditions are also obtained, which can ensure the exponential input-to-state stability of delayed Cohen–Grossberg neural networks with Markovian switching. Two numerical examples are given to illustrate the efficiency of the derived results.