title: Stability criteria for delayed neural networks.
abstract: In this paper, delay-independent global asymptotic and exponential stability for a class of delayed neural networks (DNN's) is investigated, and some criteria are established to ensure stability of DNN's by applying the Lyapunov direct method. These criteria are expressed by imposing constraints on weight matrices of the networks, and they are easy to verify and so are applicable in the design of DNN's. Comparisons between our criteria and some earlier results are also made; it is shown that our results generalize some existing criteria in the literature.
authors.affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China. htlu@mail1.sjtu.edu.cn
authors.forename: H
authors.initials: H
authors.lastname: Lu
mesh_terms: D055592:Biophysical Phenomena; D001703:Biophysics; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer