Periodicity analysis of uncertain neural networks with multiple time-varying delays

The problem of global robust periodicity is studied for a class of neural networks with norm-bounded parameter uncertainties and multiple time-varying delays. Some linear matrix inequality (LMI) representations of delay-dependent periodicity criteria are presented to guarantee the existence, uniqueness and global asymptotic stability of periodic solution for all admissible parametric uncertainties. The proposed method is based on the S-procedure and an extended integral inequality which can be deduced from the well known Leibniz-Newton formula and the Moon´s inequality. The results extend some models reported in the literature and improve conservativeness of those in the case that the derivative of the time-varying delay is assumed to be less than one.