@article{d61891c448714237845f23db14b4289d,
title = "Periodically Intermittent Stabilization of Neural Networks Based on Discrete-Time Observations",
abstract = "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. ",
keywords = "Exponential stabilization, It{\^o}'s integral, discrete-time observations, periodically intermittent control",
author = "Xiuli He and Ahn, {Choon Ki} and Peng Shi",
note = "Funding Information: Manuscript received May 18, 2020; revised June 15, 2020; accepted June 26, 2020. Date of publication June 30, 2020; date of current version November 24, 2020. This work was supported in part by Fundamental Research Funds for the Central Universities under Grant 2018B19914, and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449. This brief was recommended by Associate Editor L. A. Camunas-Mesa. (Corresponding author: Choon Ki Ahn.) Xiuli He is with the College of Science, Hohai University, Nanjing 210098, China (e-mail: hexiu00@outlook.com). Publisher Copyright: {\textcopyright} 2004-2012 IEEE.",
year = "2020",
month = dec,
doi = "10.1109/TCSII.2020.3005901",
language = "English",
volume = "67",
pages = "3497--3501",
journal = "IEEE Transactions on Circuits and Systems II: Express Briefs",
issn = "1549-7747",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",
}