title: Stochastic stability of delayed neural networks with local impulsive effects.
abstract: In this paper, the stability problem is studied for a class of stochastic neural networks (NNs) with local impulsive effects. The impulsive effects considered can be not only nonidentical in different dimensions of the system state but also various at distinct impulsive instants. Hence, the impulses here can encompass several typical impulses in NNs. The aim of this paper is to derive stability criteria such that stochastic NNs with local impulsive effects are exponentially stable in mean square. By means of the mathematical induction method, several easy-to-check conditions are obtained to ensure the mean square stability of NNs. Three examples are given to show the effectiveness of the proposed stability criterion.
authors.forename: Wenbing
authors.initials: W
authors.lastname: Zhang
authors.forename: Yang
authors.initials: Y
authors.lastname: Tang
authors.forename: Wai Keung
authors.initials: WK
authors.lastname: Wong
authors.forename: Qingying
authors.initials: Q
authors.lastname: Miao
mesh_terms: D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D013269:Stochastic Processes