Predicting House Price With a Memristor-Based Artificial Neural Network
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...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8310610/ |
| _version_ | 1818603477659549696 |
|---|---|
| author | J. J. Wang S. G. Hu X. T. Zhan Q. Luo Q. Yu Zhen Liu T. P. Chen Y. Yin Sumio Hosaka Y. Liu |
| author_facet | J. J. Wang S. G. Hu X. T. Zhan Q. Luo Q. Yu Zhen Liu T. P. Chen Y. Yin Sumio Hosaka Y. Liu |
| author_sort | J. J. Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T13:23:48Z |
| format | Article |
| id | doaj.art-d6588892140849419e16ec87dbecfb40 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:23:48Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d6588892140849419e16ec87dbecfb402022-12-21T22:30:17ZengIEEEIEEE Access2169-35362018-01-016165231652810.1109/ACCESS.2018.28140658310610Predicting House Price With a Memristor-Based Artificial Neural NetworkJ. J. Wang0S. G. Hu1X. T. Zhan2Q. Luo3Q. Yu4Zhen Liu5T. P. Chen6Y. Yin7Sumio Hosaka8Y. Liu9https://orcid.org/0000-0003-0615-7036State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, ChinaState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, ChinaState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, ChinaState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, ChinaState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Materials and Energy, Guangdong University of Technology, Guangzhou, ChinaSchool of Electrical and Electronic Engineering, Nanyang Technological University, SingaporeGraduate School of Engineering, Gunma University, Kiryu, JapanGraduate School of Engineering, Gunma University, Kiryu, JapanState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, ChinaSynaptic 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.https://ieeexplore.ieee.org/document/8310610/House price predictingneural networkmemristormemristive synapse |
| spellingShingle | J. J. Wang S. G. Hu X. T. Zhan Q. Luo Q. Yu Zhen Liu T. P. Chen Y. Yin Sumio Hosaka Y. Liu Predicting House Price With a Memristor-Based Artificial Neural Network IEEE Access House price predicting neural network memristor memristive synapse |
| title | Predicting House Price With a Memristor-Based Artificial Neural Network |
| title_full | Predicting House Price With a Memristor-Based Artificial Neural Network |
| title_fullStr | Predicting House Price With a Memristor-Based Artificial Neural Network |
| title_full_unstemmed | Predicting House Price With a Memristor-Based Artificial Neural Network |
| title_short | Predicting House Price With a Memristor-Based Artificial Neural Network |
| title_sort | predicting house price with a memristor based artificial neural network |
| topic | House price predicting neural network memristor memristive synapse |
| url | https://ieeexplore.ieee.org/document/8310610/ |
| work_keys_str_mv | AT jjwang predictinghousepricewithamemristorbasedartificialneuralnetwork AT sghu predictinghousepricewithamemristorbasedartificialneuralnetwork AT xtzhan predictinghousepricewithamemristorbasedartificialneuralnetwork AT qluo predictinghousepricewithamemristorbasedartificialneuralnetwork AT qyu predictinghousepricewithamemristorbasedartificialneuralnetwork AT zhenliu predictinghousepricewithamemristorbasedartificialneuralnetwork AT tpchen predictinghousepricewithamemristorbasedartificialneuralnetwork AT yyin predictinghousepricewithamemristorbasedartificialneuralnetwork AT sumiohosaka predictinghousepricewithamemristorbasedartificialneuralnetwork AT yliu predictinghousepricewithamemristorbasedartificialneuralnetwork |