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: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/87720 http://hdl.handle.net/10220/45488 |
| _version_ | 1811693452619939840 |
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| author | Wang, J. J. Hu, S. G. Zhan, X. T. Luo, Q. Yu, Q. Liu, Zhen Yin, Y. Hosaka, Sumio Liu, Y. Chen, Tu Pei |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wang, J. J. Hu, S. G. Zhan, X. T. Luo, Q. Yu, Q. Liu, Zhen Yin, Y. Hosaka, Sumio Liu, Y. Chen, Tu Pei |
| author_sort | Wang, J. J. |
| collection | NTU |
| 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 | 2024-10-01T06:51:54Z |
| format | Journal Article |
| id | ntu-10356/87720 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:51:54Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/877202020-03-07T13:57:31Z Predicting house price with a memristor-based artificial neural network Wang, J. J. Hu, S. G. Zhan, X. T. Luo, Q. Yu, Q. Liu, Zhen Yin, Y. Hosaka, Sumio Liu, Y. Chen, Tu Pei School of Electrical and Electronic Engineering Neural Network House Price Predicting 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. Published version 2018-08-06T08:35:23Z 2019-12-06T16:47:54Z 2018-08-06T08:35:23Z 2019-12-06T16:47:54Z 2018 Journal Article Wang, J. J., Hu, S. G., Zhan, X. T., Luo, Q., Yu, Q., Liu, Z., et al. (2018). Predicting house price with a memristor-based artificial neural network. IEEE Access, 6, 16523-16528. https://hdl.handle.net/10356/87720 http://hdl.handle.net/10220/45488 10.1109/ACCESS.2018.2814065 en IEEE Access © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 6 p. application/pdf |
| spellingShingle | Neural Network House Price Predicting Wang, J. J. Hu, S. G. Zhan, X. T. Luo, Q. Yu, Q. Liu, Zhen Yin, Y. Hosaka, Sumio Liu, Y. Chen, Tu Pei Predicting house price with a memristor-based artificial neural network |
| 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 | Neural Network House Price Predicting |
| url | https://hdl.handle.net/10356/87720 http://hdl.handle.net/10220/45488 |
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