Robust Multimode Function Synchronization of Memristive Neural Networks with Parameter Perturbations and Time-Varying Delays
- Submitting institution
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University of Hertfordshire
- Unit of assessment
- 12 - Engineering
- Output identifier
- 22633457
- Type
- D - Journal article
- DOI
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10.1109/TSMC.2020.2997930
- Title of journal
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Article number
- TSMC.2020.2997930
- First page
- -
- Volume
- 2020
- Issue
- -
- ISSN
- 2168-2216
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2020
- URL
-
-
- 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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3
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The research leading to the presented outputs was funded by the National Natural Science Foundation of China (Grant 61971185) as a result of an international collaboration between the University of Hertfordshire (UH) and Hunan University, China where UH was the hosting institution. Memristors are widely viewed as the future of non-volatile memories, AI hardware, and brain-inspired computing. The paper demonstrates complete synchronisation in memristive neural networks, providing, for the first time, a mathematical expression which can be used to model multimode synchronisation in real network deployments. Of significance is also the implementation of new adaptive controllers using electronic circuits
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -