Stochastic synchronization for Markovian coupled neural networks with partial information on transition probabilities

This paper focuses on stochastic synchronization for Markovian coupled neural networks with partial information on transition probabilities and random coupling strengths. The coupling configuration matrices are not restricted to be symmetric, and the coupling strengths are mutually independent random variables. By designing a novel augmented Lyapunov–Krasovskii functional and using reciprocally convex combination technique and the properties of random variables, new delay-dependent synchronization criteria in terms of linear matrix inequalities are derived. The obtained criteria depend not only on upper and lower bounds of delay but also on mathematical expectations and variances of random coupling strengths. Numerical examples are provided to verify the effectiveness of the presented results.