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.

引用

页数：4

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