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...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Results in Engineering
المؤلفون الرئيسيون: Hening Qin, Zhiqiang Liao, Hitoshi Tabata
التنسيق: مقال
اللغة:الإنجليزية
منشور في: Elsevier 2024-12-01
الموضوعات:
Magnetic tunnel junctions
Radial basis function
Time series prediction
Temperature effect
الوصول للمادة أونلاين:http://www.sciencedirect.com/science/article/pii/S2590123024016244
الوصف
الملخص: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.
تدمد:2590-1230

مواد مشابهة