General memristor with applications in multilayer neural networks

Wen, S; Xie, X; Yan, Z; Huang, T; Zeng, Z

General memristor with applications in multilayer neural networks

Wen, S Xie, X Yan, Z

Huang, T Zeng, Z

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Publication Type:: Journal Article
Citation:: Neural Networks, 2018, 103 pp. 142 - 149
Issue Date:: 2018-07-01

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Field	Value	Language
dc.contributor.author	Wen, S	en_US
dc.contributor.author	Xie, X	en_US
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Huang, T	en_US
dc.contributor.author	Zeng, Z	en_US
dc.date.available	2018-03-19	en_US
dc.date.issued	2018-07-01	en_US
dc.identifier.citation	Neural Networks, 2018, 103 pp. 142 - 149	en_US
dc.identifier.issn	0893-6080	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131981
dc.description.abstract	© 2018 Elsevier Ltd Memristor describes the relationship between charge and flux. Although several window functions for memristors based on the HP linear and nonlinear dopant drift models have been studied, most of them are inadequate to capture the full characteristics of memristors. To address this issue, this paper proposes a unified window function to describe a general memristor with restrictions of its parameters given. Compared with other window functions, the proposed function demonstrates high validity and accuracy. In order to make the simulation results have high consistency with the results of actual circuit, we apply the new window function to the simulation of a memristor-based multilayer neural network (MNN) circuit. The overall accuracy will vary with the change of control parameters in the window function. It implies that the proposed model can guide the design of actual memristor-based circuits.	en_US
dc.relation.ispartof	Neural Networks	en_US
dc.relation.isbasedon	10.1016/j.neunet.2018.03.015	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Nonlinear Dynamics	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	General memristor with applications in multilayer neural networks	en_US
dc.type	Journal Article
utslib.citation.volume	103	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	103	en_US

Abstract:

© 2018 Elsevier Ltd Memristor describes the relationship between charge and flux. Although several window functions for memristors based on the HP linear and nonlinear dopant drift models have been studied, most of them are inadequate to capture the full characteristics of memristors. To address this issue, this paper proposes a unified window function to describe a general memristor with restrictions of its parameters given. Compared with other window functions, the proposed function demonstrates high validity and accuracy. In order to make the simulation results have high consistency with the results of actual circuit, we apply the new window function to the simulation of a memristor-based multilayer neural network (MNN) circuit. The overall accuracy will vary with the change of control parameters in the window function. It implies that the proposed model can guide the design of actual memristor-based circuits.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/131981