期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 49, 期 9, 页码 1787-1796出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2825021
关键词
Echo state network; least mean quare; memristor; neural network
资金
- Natural Science Foundation of China [61673187, 61673188, 61773081]
- Technology Transformation Program of Chongqing Higher Education University [KJZH17102]
- NPRP from the Qatar National Research Fund (a member of Qatar Foundation) [NPRP 9-466-1-103]
In this paper, we propose a novel computational architecture of memristor-based echo state network (MESN) with the online least mean square (LMS) algorithm. Newman and Watts small-world network is adopted for the topological structure of MESN network with memristive neural synapses. In the MESN network, the state matrix of the reservoir layer, which is obtained by raising the dimension of input data, is utilized as an input of the LMS algorithm to train the output weight matrix on chip. After certain iterations, the resistance value of memristor is adjusted to a constant. Thus, the final weight output matrix is obtained. To verify the effectiveness of the proposed MESN network, car evaluation and short-term power load forecasting are employed with the effect evaluation of the node number and the connectivity degree of the reservoir layer. The research provides a novel way to design neuromorphic computing systems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据