Delay-dependent robust dissipativity conditions for delayed neural networks with random uncertainties

This paper deals with the problem of the robust dissipativity analysis for delayed neural networks with randomly occurring uncertainties. The randomly occurring uncertainties under consideration are assumed to follow certain mutually uncorrelated Bernoulli distributed white noise sequences. By using reciprocally convex approach combined with an extended Wirtinger inequality, some delay-dependent conditions for the concerned neural networks to be stochastically strictly (Q, S, R)-θ-dissipative are established. Finally, two numerical examples are given to illustrate the reduced conservatism and effectiveness of our proposed approach.