A neuromorphic radial-basis-function net using magnetic bits for time series prediction

被引：0
|
作者：
Qin, Hening [1 ]
Liao, Zhiqiang [1 ,2 ]
Tabata, Hitoshi [1 ,2 ]
机构：
[1] Univ Tokyo, Grad Sch Engn, Dept Elect Engn & Informat Syst, 7-3-1 Hongo,Bunkyo Ku, Tokyo 1138656, Japan
[2] Univ Tokyo, Grad Sch Engn, Dept Bioengn, 7-3-1 Hongo,Bunkyo Ku, Tokyo 1138656, Japan
来源：
RESULTS IN ENGINEERING | 2024年 / 24卷
关键词：
Magnetic tunnel junctions; Radial basis function; Time series prediction; Temperature effect; SHORT-TERM-MEMORY;
D O I：
10.1016/j.rineng.2024.103371
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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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页数：4
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