Event-triggered state estimation for complex networks with mixed time delays via sampled data information: The continuous-time case
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 031-120548-7002897
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2014.2386781
- Title of journal
- Ieee Transactions On Cybernetics
- Article number
- -
- First page
- 2804
- Volume
- 45
- Issue
- 12
- ISSN
- 2168-2267
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 151
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper that deals with the event-triggered state estimation problem for continuous-time complex networks with sampled measurements. A novel sampled-data-based state estimator is developed for continuous-time complex networks to handle the estimation issue subject to the event-triggered mechanism. This paper was published in the No. 1 journal in Computer Science: Cybernetics and recognised as a Highly Cited Paper by the Web of Science.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
