Periodically Intermittent Stabilization of Neural Networks Based on Discrete-Time Observations
- Authors
- He, Xiuli; Ahn, Choon Ki; Shi, Peng
- Issue Date
- 12월-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Exponential stabilization; periodically intermittent control; discrete-time observations; Ito' s integral
- Citation
- IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, v.67, no.12, pp.3497 - 3501
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
- Volume
- 67
- Number
- 12
- Start Page
- 3497
- End Page
- 3501
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/51293
- DOI
- 10.1109/TCSII.2020.3005901
- ISSN
- 1549-7747
- 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.
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