Predicting House Price With a Memristor-Based Artificial Neural Network
Predicting House Price With a Memristor-Based Artificial Neural Network
复制标题
使用基于忆阻器的人工神经网络预测房价
DOI：
10.1109/access.2018.2814065
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发表时间：
2018-03
期刊：
影响因子：
3.9
通讯作者：
Liu Y.
中科院分区：
文献类型：
--
作者：
Wang J.J.;Hu S.G.;Zhan X.T.;Luo Q.;Yu Q.;Liu Z.;Chen T.P.;Yin Y.;Hosaka S.;Liu Y.
突触记忆阻器因其在人工神经网络(ANN)中的潜在应用而备受关注。然而，这种基于忆阻器的网络在现实生活中的有用应用很少有报道。本文设计了一种基于记忆阻器的神经网络，用BP算法学习多变量回归模型。介绍了一种基于记忆阻器的重量单位电路，可编程为兴奋性突触或抑制性突触。电子突触的重量由忆阻器的电导决定，突触的电流遵循电荷依赖关系。人工神经网络具有从标记样本中学习的能力，并在在线训练后做出预测。以美国波士顿几个城镇的房价为例，用人工神经网络学习回归模型，预测结果与目标数据较为接近。
Synaptic memristor has attracted much attention for its potential applications in artificial neural networks (ANNs). However useful applications in real life with such memristor-based networks have seldom been reported. In this paper, an ANN based on memristors is designed to learn a multi-variable regression model with a back-propagation algorithm. A weight unit circuit based on memristor, which can be programed as an excitatory synapse or inhibitory synapse, is introduced. The weight of the electronic synapse is determined by the conductance of the memristor, and the current of the synapse follows the charge-dependent relationship. The ANN has the ability to learn from labeled samples and make predictions after online training. As an example, the ANN was used to learn a regression model of the house prices of several Boston towns in the USA and the predicted results are found to be close to the target data.
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