Lag synchronization of switched neural networks via neural activation function and applications in image encryption
- Submitting institution
-
Loughborough University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1931
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2014.2387355
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 1493
- Volume
- 26
- Issue
- 7
- ISSN
- 2162-237X
- 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
-
4
- Research group(s)
-
-
- Citation count
- 150
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work has impacted the state of the art through a novel approach to designing neural network controllers based on neuron activation function, solving the problem of global exponential lag synchronization for switched neural networks with the challenging condition of time-varying delays.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -