Training Recurrent Neural Networks with the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 746
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2014.2361267
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 1900
- Volume
- 26
- Issue
- 9
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2014
- 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
-
4
- Research group(s)
-
-
- Citation count
- 38
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces a series of improvements in the application of Recurrent Neural Networks to control connected converters in electric power systems that include smart grids, renewable energy resources and energy storage devices. It was mentioned as one of the major innovations in Reinforcement Learning at the keynote lecture of the IEEE World Congress on Computational Intelligence, Beijing July 2014, and implemented as part of the H2020-EE-2015-2-RIA project on Innovative Technology for District Heating and Cooling (InDeal, 2016-2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
