A Noise-Tolerant Zeroing Neural Network for Time-Dependent Complex Matrix Inversion Under Various Kinds of Noises
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1445
- Type
- D - Journal article
- DOI
-
10.1109/tii.2019.2936877
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- -
- First page
- 3757
- Volume
- 16
- Issue
- 6
- ISSN
- 1551-3203
- Open access status
- Technical exception
- Month of publication
- June
- Year of publication
- 2020
- 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
-
5
- Research group(s)
-
D - Robotics and Embedded Systems (RES)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper, in IEEE-TII, a leading-journal, innovatively develops a complex-valued noise-tolerant Zeroing- neural-network (ZNN) using an integral-type formula, significantly extending previous work on ZNNs. Significantly, the proposed method provides effective mathematical foundations to solve complex-valued time-dependent matrix inversion. The work has broad applicability such as in robotic control and computer vision. This research, supported by NNSFC, China, has impacted on the design of ZNNs, is cited by the inventor of the ZNN and contributed to Li's nomination to the 2020 Blavatnik Awards for Young Scientists in the UK. Additionally, it led to invited talks at universities in China and Australia.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
