Memory Analysis for Memristors and Memristive Recurrent Neural Networks

Gang Bao; Yide Zhang; Zhigang Zeng

doi:10.1109/JAS.2019.1911828

Volume 7 Issue 1

Jan. 2020

IEEE/CAA Journal of Automatica Sinica

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Gang Bao, Yide Zhang and Zhigang Zeng, "Memory Analysis for Memristors and Memristive Recurrent Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 96-105, Jan. 2020. doi: 10.1109/JAS.2019.1911828

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Gang Bao, Yide Zhang and Zhigang Zeng, "Memory Analysis for Memristors and Memristive Recurrent Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 96-105, Jan. 2020. doi: 10.1109/JAS.2019.1911828

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Memory Analysis for Memristors and Memristive Recurrent Neural Networks

doi: 10.1109/JAS.2019.1911828

Funds: The work was supported by the National Natural Science Foundation of China (618760 97, 61673188, 61761130081), the National Key Research and Development Program of China (2016YFB0800402), the Foundation for Innovative Research Groups of Hubei Province of China (2017CFA005), and the Fundamental Research Funds for the Central Universities (2017KFXKJC002)

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Author Bio:
Gang Bao (M’19) received the B.S. degree in mathematics from Hubei Normal University, Huangshi, China, the M.S. degree in applied mathematics from Beijing University of Technology, Beijing, China, in 2000 and 2004, the Ph.D. degree from the Department of Control Science and Engineering, Huazhong University of Science and Technology, respectively. Now he is working at Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control Electrical Engineering and New Energy School, China Three Gorges University

Yide Zhang (S’13) received the B.E. degree in automation from Huazhong University of Science and Technology, China, in 2014, and the Ph.D. degree in electrical engineering from the University of Notre Dame, USA, in 2019. He is currently a Postdoctoral Scholar with the California Institute of Technology. His research is interdisciplinary and focused on developing new types of optical imaging techniques that could advance the work of other researchers and medical personnel in a wide variety of fields. His research interests include multiphoton microscopy, fluorescence lifetime imaging microscopy, super-resolution microscopy, photoacoustic tomography, high-speed imaging, deep tissue imaging, adaptive optics, computational imaging, and deep learning

Zhigang Zeng (F’20) received the Ph.D. degree in systems analysis and integration from Huazhong University of Science and Technology, Wuhan, China, in 2003. He is currently a Professor with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China, and also with the Key Laboratory of Image Processing and Intelligent Control of the Education Ministry of China, Wuhan, China. He has been an Associate Editor of the IEEE Transactions on Neural Networks (2010 – 2011), IEEE Transactions on Cybernetics (since 2014), IEEE Transactions on Fuzzy Systems (since 2016), and a member of the Editorial Board of Neural Networks (since 2012), Cognitive Computation (since 2010), Applied Soft Computing (since 2013). He has published over 200 international journal papers. His current research interests include theory of functional differential equations and differential equations with discontinuous right-hand sides, and their applications to dynamics of neural networks, memristive systems, and control systems
Corresponding author: G. Bao is with Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control, Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, and Hubei Key Laboratory of Applied Mathematics (Hubei University), Wuhan 430074, China (e-mail: ctgugangbao@ctgu.edu.cn)
Received Date: 2019-08-09
Accepted Date: 2019-10-24

Available Online: 2019-11-12

Abstract

Abstract

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers. Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis, i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors' mathematical models with linear and nonlinear drift. Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.
- Dopant drift,
- memory,
- memristive neural networks,
- memristor

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References(34)

References

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Memory Analysis for Memristors and Memristive Recurrent Neural Networks

doi: 10.1109/JAS.2019.1911828

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