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.