Stability analysis of stochastic neural networks with Markovian jump parameters using delay-partitioning approach

In this paper, the problem of mean square asymptotic stability of stochastic neural networks with Markovian jumping parameters is considered. By choosing an augmented Lyapunov–Krasovskii functional and utilizing the delay-partitioning method, novel delay-dependent mean square asymptotic stability conditions are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the proposed approach.