title: Improved global robust asymptotic stability criteria for delayed cellular neural networks.
abstract: This paper considers the problem of global robust stability analysis of delayed cellular neural networks (DCNNs) with norm-bounded parameter uncertainties. In terms of a linear matrix inequality, a new sufficient condition ensuring a nominal DCNN to have a unique equilibrium point which is globally asymptotically stable is proposed. This condition is shown to be a generalization and improvement over some previous criteria. Based on the stability result, a robust stability condition is developed, which contains an existing robust stability result as a special case. An example is provided to demonstrate the reduced conservativeness of the proposed results.
authors.forename: Shengyuan
authors.initials: S
authors.lastname: Xu
authors.forename: James
authors.initials: J
authors.lastname: Lam
authors.forename: Daniel W C
authors.initials: DW
authors.lastname: Ho
authors.forename: Yun
authors.initials: Y
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D013997:Time Factors