title: Globally asymptotic stability of a class of neutral-type neural networks with delays.
abstract: Several stability conditions for a class of systems with retarded-type delays are presented in the literature. However, no results have yet been presented for neural networks with neutral-type delays. Accordingly, this correspondence investigates the globally asymptotic stability of a class of neutral-type neural networks with delays. This class of systems includes Hopfield neural networks, cellular neural networks, and Cohen-Grossberg neural networks. Based on the Lyapunov stability method, two delay-independent sufficient stability conditions are derived. These stability conditions are easily checked and can be derived from the connection matrix and the network parameters without the requirement for any assumptions regarding the symmetry of the interconnections. Two illustrative examples are presented to demonstrate the validity of the proposed stability criteria.
authors.forename: Chao-Jung
authors.initials: CJ
authors.lastname: Cheng
authors.forename: Teh-Lu
authors.initials: TL
authors.lastname: Liao
authors.forename: Jun-Juh
authors.initials: JJ
authors.lastname: Yan
authors.forename: Chi-Chuan
authors.initials: CC
authors.lastname: Hwang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013997:Time Factors