Enhanced cubic function negative-determination Lemma on stability analysis for delayed neural networks via new analytical techniques

Abstract

This brief studies the stability issue of neural networks with respect to time-varying delay. First, an enhanced cubic functions negative-definiteness determination Lemma is proposed by using partitioning technique and Taylor’s formula. Compared with the previous results, this Lemma can eliminate the conservatism of constraint conditions by interval-decomposition method. Second, for the sake of the relaxed positive-definiteness determination, an asymmetric Lyapunov–Krasovskii functional is presented. Third, a resulting time-varying delay-dependent stability criterion is derived by making use of some inequality techniques. Finally, the advantage of the new approach are verified theoretically and numerically.