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
收藏
页数：4
相关论文
共 50 条
  • [1] Time Series Prediction Using Focused Time Lagged Radial Basis Function Network
    Kumar, Rajesh
    Srivastava, Smriti
    Gupta, J. R. P.
    2016 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY (INCITE) - NEXT GENERATION IT SUMMIT ON THE THEME - INTERNET OF THINGS: CONNECT YOUR WORLDS, 2016,
  • [2] Recurrent radial basis function network for time-series prediction
    Zemouri, R
    Racoceanu, D
    Zerhouni, N
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2003, 16 (5-6) : 453 - 463
  • [3] Time Series Prediction Using Radial Basis Function Network with Transformation of Training Data and Its Applications
    Automatic Control and Computer Sciences, 2022, 56 : 239 - 252
  • [4] Time Series Prediction Using Radial Basis Function Network with Transformation of Training Data and Its Applications
    Kitayama, Satoshi
    Saito, Kohei
    AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2022, 56 (03) : 239 - 252
  • [5] Time series prediction using evolving radial basis function networks with new encoding scheme
    Du, Haiping
    Zhang, Nong
    NEUROCOMPUTING, 2008, 71 (7-9) : 1388 - 1400
  • [6] A programmable analog radial-basis-function based classifier
    Peng, Sheng-Yu
    Tsao, Yu
    Hasler, Paul E.
    Anderson, David V.
    2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, : 1425 - 1428
  • [7] Sea-clutter modeling using a radial-basis-function neural network
    Hennessey, G
    Leung, H
    Drosopoulos, A
    Yip, PC
    IEEE JOURNAL OF OCEANIC ENGINEERING, 2001, 26 (03) : 358 - 372
  • [8] Long-Term Time-Series Prediction Using Radial Basis Function Neural Networks
    Alexandridis, Alex
    Famelis, Ioannis Th.
    Tsitouras, Charalambos
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014), 2015, 1648
  • [9] Dual-orthogonal radial basis function networks for nonlinear time series prediction
    Billings, SA
    Hong, X
    NEURAL NETWORKS, 1998, 11 (03) : 479 - 493
  • [10] A dynamic regularized radial basis function network for nonlinear, nonstationary time series prediction
    Yee, P
    Haykin, S
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1999, 47 (09) : 2503 - 2521
12345