title: Deriving sufficient conditions for global asymptotic stability of delayed neural networks via nonsmooth analysis--II.
abstract: Following our recent approach of nonsmooth analysis, we report a new set of sufficient conditions and its implications for the global asymptotic stability of delayed cellular neural networks (DCNN). The new conditions not only unify a string of previous stability results, but also yield strict improvement over them by allowing the symmetric part of the feedback matrix positive definite, hence enlarging the application domain of DCNNs. Advantages of the new results over existing ones are illustrated with examples. We also compare our results with those related results obtained via LMI approach.
authors.forename: Houduo
authors.initials: H
authors.lastname: Qi
authors.forename: Liqun
authors.initials: L
authors.lastname: Qi
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors