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

Xiuli He; Choon Ki Ahn; Peng Shi

doi:10.1109/TCSII.2020.3005901

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

Xiuli He, Choon Ki Ahn, Peng Shi

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

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.

Original language	English
Article number	9129843
Pages (from-to)	3497-3501
Number of pages	5
Journal	IEEE Transactions on Circuits and Systems II: Express Briefs
Volume	67
Issue number	12
DOIs	https://doi.org/10.1109/TCSII.2020.3005901
Publication status	Published - 2020 Dec

Bibliographical 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: [email protected]).

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

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

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/TCSII.2020.3005901

Cite this

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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: [email protected]). Publisher Copyright: {\textcopyright} 2004-2012 IEEE.",

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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