Stochastic Stability of Delayed Neural Networks with Local Impulsive Effects

Wenbing Zhang, Yang Tang, Wai Keung Wong, Qingying Miao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

32 Citations (Scopus)

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.
Original languageEnglish
Article number06998862
Pages (from-to)2336-2345
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number10
DOIs
Publication statusPublished - 1 Oct 2015

Keywords

  • Impulsive systems
  • local impulsive effects
  • neural networks (NNs)
  • stability analysis

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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