A fast adaptive tunable RBF network for nonstationary systems
- Submitting institution
-
Loughborough University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 516
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2015.2484378
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 2683
- Volume
- 46
- Issue
- 12
- ISSN
- 2168-2267
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- 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)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has impacted various works such as the self-organizing neural network (Han, Beijing Univ of Technology), the fast online identification (Hu, Zhejiang Univ), and the NARX model (Inoaka, Kitasato University). This work also led to a PhD project in the adaptive modelling in the vehicle applications in 2017.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
