Lagrange stability for delayed recurrent neural networks with Markovian switching based on stochastic vector Halandy inequalities

Abstract In this brief, the mean square Lagrange stability for delayed recurrent neural networks with Markovian switching is studied. Firstly, stochastic vector Halandy inequalities are established by virtue of stochastic analysis and M-matrix techniques. Secondly, without additional restrictive conditions on the time-varying delay, the criteria on mean square Lagrange stability for delayed recurrent neural networks with Markovian switching are derived by means of the vector Halandy inequalities, and the global attractive sets in mean square sense are given. A numerical example is provided to examine the correctness of the derived results.