Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks with Stochastic Parameters and Incomplete Measurements
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 042-177363-5807
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2016.2516030
- Title of journal
- Ieee Transactions On Neural Networks And Learning Systems
- Article number
- -
- First page
- 1152
- Volume
- 28
- Issue
- 5
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- URL
-
https://bura.brunel.ac.uk/handle/2438/21747
- 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
-
2
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 124
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has been published in the No. 2 journal (out of 103) in computer science (theory and methods) according to ISI Web of knowledge. A novel concept of event-triggered estimation is proposed to reflect the energy-saving need for monitoring recurrent neural networks (RNNS) and a novel methodology is developed by exploiting a time-varying real-valued function, the Kronecker product, as well as the recursive linear matrix inequalities. This ‘highly cited paper’ of IEEE-T-NNLS has opened up a brand new research venue for resource-constrained estimation of RNNS, and the proposed concept/methodology has been later adopted/exploited in many publications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
