A neuromorphic radial-basis-function net using magnetic bits for time series prediction Article Swipe
Hengyu Qin
,
Zhiqiang Liao
,
Hitoshi Tabata
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2024.103371
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2024.103371
Magnetic tunnel junctions (MTJs) are considered strong candidates for constructing neuromorphic systems owing to their low power consumption and high integrability. However, research on MTJ-based local approximation network is still lacking. In this work, we propose an MTJ-based radial basis function (RBF) network and numerically investigate its time-series prediction capability. The results demonstrate that the MTJ-based RBF network can enhance its prediction performance by utilizing increased environmental temperatures, achieving performance better than traditional software artificial neural networks.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.rineng.2024.103371
- OA Status
- gold
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404135961
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- OpenAlex ID
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https://openalex.org/W4404135961Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.rineng.2024.103371Digital Object Identifier
- Title
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A neuromorphic radial-basis-function net using magnetic bits for time series predictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-07Full publication date if available
- Authors
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Hengyu Qin, Zhiqiang Liao, Hitoshi TabataList of authors in order
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https://doi.org/10.1016/j.rineng.2024.103371Publisher landing page
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.rineng.2024.103371Direct OA link when available
- Concepts
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Neuromorphic engineering, Series (stratigraphy), Net (polyhedron), Radial basis function, Basis (linear algebra), Function (biology), Computer science, Artificial intelligence, Algorithm, Mathematics, Artificial neural network, Biology, Evolutionary biology, Paleontology, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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14Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.networks. | 76 |
| abstract_inverted_index.utilizing | 64 |
| abstract_inverted_index.artificial | 74 |
| abstract_inverted_index.candidates | 7 |
| abstract_inverted_index.considered | 5 |
| abstract_inverted_index.prediction | 48, 61 |
| abstract_inverted_index.capability. | 49 |
| abstract_inverted_index.consumption | 17 |
| abstract_inverted_index.demonstrate | 52 |
| abstract_inverted_index.investigate | 45 |
| abstract_inverted_index.numerically | 44 |
| abstract_inverted_index.performance | 62, 69 |
| abstract_inverted_index.time-series | 47 |
| abstract_inverted_index.traditional | 72 |
| abstract_inverted_index.constructing | 9 |
| abstract_inverted_index.neuromorphic | 10 |
| abstract_inverted_index.approximation | 26 |
| abstract_inverted_index.environmental | 66 |
| abstract_inverted_index.temperatures, | 67 |
| abstract_inverted_index.integrability. | 20 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.21623277 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |