An Improved Complex-Valued Recurrent Neural Network Model for Time-Varying Complex-Valued Sylvester Equation
- Submitting institution
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Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 6912608
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2019.2896983
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 19291
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2019
- URL
-
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- Supplementary information
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- 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
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5
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes an improved complex-valued Zhang neural network (ICZNN) model for tackling the complex-valued time-varying Sylvester equation (CVTVSE), and can potentially improve industrial applications of robotics and communications. It has been cited and extended by researchers investigating further models for time-varying linear matrix equations (Xiao et al, 2019, 10.1109/ACCESS.2019.2941961) and finite-time convergent complex-valued zeroing neural network models (Jian et al, 2020, 10.1109/TII.2019.2941750).
- Author contribution statement
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- Non-English
- No
- English abstract
- -