title: Global exponential convergence of Cohen-Grossberg neural networks with time delays.
abstract: In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.
authors.forename: Hongtao
authors.initials: H
authors.lastname: Lu
authors.forename: Ruiming
authors.initials: R
authors.lastname: Shen
authors.forename: Fu-Lai
authors.initials: FL
authors.lastname: Chung
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D013997:Time Factors