New stochastic robust stability criteria for time-varying delay neural networks with Markovian jump parameters

In this paper, the problem of stochastic robust stability of interval time-varying delay neural networks with Markovian jump parameters is investigated. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. The linear factional uncertainty is considered, it means that a less conservative result will be obtained than using norm-bounded parameter uncertainties. And the derivative of the delay function can exceed one. Based on the lyapunov-krasovskii functional approach, a new delay-dependent stochastic stability criteria is presented in terms of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.