title: Global exponential stability and periodic solutions of cellular neural networks with delay.
abstract: In this paper, some sufficient conditions for the global exponential stability and the existence of periodic solutions of cellular neural networks with delay (DCNN) model are obtained by means of a Lyapunov functional approach. These conditions can be used to design globally stable DCNN's and periodic oscillatory DCNN's and thus have important significance in both theory and applications.
authors.affiliation: Center for Nonlinear Science Studies, Kunming University of Science and Technology, Kunming 650093, People's Republic of China. huifangkm@ynmail.com
authors.forename: H
authors.initials: H
authors.lastname: Fang
authors.forename: J
authors.initials: J
authors.lastname: Li
mesh_terms: D000818:Animals; D055592:Biophysical Phenomena; D001703:Biophysics; D006801:Humans; D008959:Models, Neurological; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology*; D010507:Periodicity