A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion
- Submitting institution
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Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 7314199
- Type
- D - Journal article
- DOI
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10.1016/j.neunet.2019.05.005
- Title of journal
- Neural Networks
- Article number
- -
- First page
- 124
- Volume
- 117
- Issue
- -
- ISSN
- 0893-6080
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2019
- URL
-
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- Supplementary information
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- Request cross-referral to
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- 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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8
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a new zeroing neural network (ZNN) using a versatile activation function (VAF) for solving time-dependent matrix inversion. The work has informed further research by the authors on nonlinearly activated ZNN models (Zeng et al, 2020, 10.1016/j.neucom.2020.01.070) and by other research groups, e.g. on time-varying convex optimization problems (Shao et al, 2020, 10.1016/j.neucom.2020.06.051). The work has impact potential in the area of self-learning and mobile robotics and other application areas requiring learning of complex inverse dynamics, such as advanced grinding and finishing.
- Author contribution statement
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- Non-English
- No
- English abstract
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