RBFNN-Based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 44
- Type
- D - Journal article
- DOI
-
10.1109/TAC.2019.2914257
- Title of journal
- IEEE Transactions on Automatic Control
- Article number
- -
- First page
- 376
- Volume
- 65
- Issue
- 1
- ISSN
- 0018-9286
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/document/8703864
- 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)
-
-
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents an entropy-based filtering framework using neural networks for complex stochastic nonlinear systems. This paper is significant because the Gaussian assumption has been released for non-Gaussian dynamics filtering with an analytical structure and non-integer order entropy formula, also this work was adopted for electric-arc furnace monitoring in practice. Material related to this paper has been presented at the 2nd International Forum on Frontiers of Automation and Artificial Intelligence as an invited note in Shenyang, China in 2020.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
