A neuromorphic radial-basis-function net using magnetic bits for time series prediction
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 functio...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024016244 |
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| author | Hening Qin Zhiqiang Liao Hitoshi Tabata |
| author_facet | Hening Qin Zhiqiang Liao Hitoshi Tabata |
| author_sort | Hening Qin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1ff6ec6300a94da09d882d57e552e247 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-1ff6ec6300a94da09d882d57e552e2472024-12-19T10:59:11ZengElsevierResults in Engineering2590-12302024-12-0124103371A neuromorphic radial-basis-function net using magnetic bits for time series predictionHening Qin0Zhiqiang Liao1Hitoshi Tabata2Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, JapanDepartment of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Corresponding authors at: Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Corresponding authors at: Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.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.http://www.sciencedirect.com/science/article/pii/S2590123024016244Magnetic tunnel junctionsRadial basis functionTime series predictionTemperature effect |
| spellingShingle | Hening Qin Zhiqiang Liao Hitoshi Tabata A neuromorphic radial-basis-function net using magnetic bits for time series prediction Results in Engineering Magnetic tunnel junctions Radial basis function Time series prediction Temperature effect |
| title | A neuromorphic radial-basis-function net using magnetic bits for time series prediction |
| title_full | A neuromorphic radial-basis-function net using magnetic bits for time series prediction |
| title_fullStr | A neuromorphic radial-basis-function net using magnetic bits for time series prediction |
| title_full_unstemmed | A neuromorphic radial-basis-function net using magnetic bits for time series prediction |
| title_short | A neuromorphic radial-basis-function net using magnetic bits for time series prediction |
| title_sort | neuromorphic radial basis function net using magnetic bits for time series prediction |
| topic | Magnetic tunnel junctions Radial basis function Time series prediction Temperature effect |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024016244 |
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