Robust stability analysis for discrete-time neural networks with time-varying leakage delays and random parameter uncertainties

•Robust stability problem for discrete-time neural network with leakage delays is investigated.•The random parameter uncertainties are characterized by Bernoulli distribution..•Based on LKF method, sufficient conditions for stability are given in terms of LMIs•Numerical simulations are exploited to illustrate the theoretical results.

This paper is concerned with the problem of robust stability analysis for discrete-time neural networks with time-varying coupling delays, random parameter uncertainties and time-varying leakage delays. The uncertainties enter into the system parameters in a random way and such randomly occurring uncertainties obey certain mutually uncorrelated Bernoulli-distributed white noise sequences. The important feature of the results reported here is that the probability of occurrence of the parameter uncertainties are known a priori. Constructing suitable Lyapunov–Krasovskii functional (LKF) terms, sufficient conditions ensuring the stability of the discrete-time neural networks are derived in terms of linear matrix inequalities (LMIs). Finally, numerical examples are rendered to exemplify the effectiveness of the proposed results.