Periodically Intermittent Stabilization of Neural Networks Based on Discrete-Time Observations

Xiuli He, Choon Ki Ahn, Peng Shi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this brief, we design a periodically intermittent controller to stabilize a class of networks by using discrete-time observations on the states of white noise, which will cut costs by decreasing observation frequency and controlled time. The supremum of discrete-time observations is derived by a transcendental equation. Sufficient conditions are obtained to exponentially stabilize the underlying networks. A numerical example is provided to illustrate the effectiveness and advantages of the proposed new design technique.

Original languageEnglish
Article number9129843
Pages (from-to)3497-3501
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Issue number12
Publication statusPublished - 2020 Dec


  • Exponential stabilization
  • Itô's integral
  • discrete-time observations
  • periodically intermittent control

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


Dive into the research topics of 'Periodically Intermittent Stabilization of Neural Networks Based on Discrete-Time Observations'. Together they form a unique fingerprint.

Cite this