title: Stability analysis for neural networks with time-varying delay based on quadratic convex combination.
abstract: In this paper, a novel method is developed for the stability problem of a class of neural networks with time-varying delay. New delay-dependent stability criteria in terms of linear matrix inequalities for recurrent neural networks with time-varying delay are derived by the newly proposed augmented simple Lyapunov-Krasovski functional. Different from previous results by using the first-order convex combination property, our derivation applies the idea of second-order convex combination and the property of quadratic convex function which is given in the form of a lemma without resorting to Jensen's inequality. A numerical example is provided to verify the effectiveness and superiority of the presented results.
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang
authors.forename: Feisheng
authors.initials: F
authors.lastname: Yang
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Liu
authors.forename: Qingling
authors.initials: Q
authors.lastname: Zhang
mesh_terms: D016571:Neural Networks, Computer; D013997:Time Factors