TY - JOUR
T1 - Periodically Intermittent Stabilization of Neural Networks Based on Discrete-Time Observations
AU - He, Xiuli
AU - Ahn, Choon Ki
AU - Shi, Peng
N1 - Funding Information:
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.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - discrete-time observations
KW - Exponential stabilization
KW - Itô's integral
KW - periodically intermittent control
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U2 - 10.1109/TCSII.2020.3005901
DO - 10.1109/TCSII.2020.3005901
M3 - Article
AN - SCOPUS:85097075590
VL - 67
SP - 3497
EP - 3501
JO - IEEE Transactions on Circuits and Systems I: Regular Papers
JF - IEEE Transactions on Circuits and Systems I: Regular Papers
SN - 1549-8328
IS - 12
M1 - 9129843
ER -