Stability analysis for stochastic neural networks of neutral type with both Markovian jump parameters and mixed time delays

In this paper, the global asymptotic stability is investigated for stochastic neural networks of neutral type with both Markovian jump parameters and mixed time delays. The jumping parameters are modeled as a continuous-time, finite-state Markov chain. By employing the Lyapunov–Krasovskii functional and stochastic analysis theory as well as linear matrix inequality technique, some novel sufficient conditions are derived to guarantee the global asymptotic stability of the equilibrium point in the mean square. The proposed model of neutral type is quite general since many factors such as noise perturbations, Markovian jump parameters and mixed time delays are considered in this paper. The activation functions in this paper may be neither monotonically increasing nor continuously differentiable, and they are more general than those usual Lipschitz conditions. The results obtained in this paper comprise and generalize those given in the previous literature. Two numerical examples are provided to show the effectiveness of the theoretical results.