Stability analysis of stochastic Markovian switching static neural networks with asynchronous mode-dependent delays
This paper is concerned with the asymptotic stability analysis for stochastic static neural networks with mode-dependent time-varying delays, in which the delay modes and the system modes are asynchronous. That is, they depend on different jumping modes. In addition, the derivatives of the mode-dependent time-varying delays are no longer required to be smaller than one. By constructing new LyapunovKrasovskii functional and combining with a convex polyhedron method, several delay-dependent stability conditions are formulated based on linear matrix inequalities (LMIs). The usefulness of the proposed approach are finally demonstrated by two numerical examples.
markovian switching mode-dependent delay asymptotic stability stochastic static neural networks liner matrix inequality (lmi)