忆阻递归神经网络稳定性分析及其在联想记忆中的应用

Bao Gang; Chen Yuanyuan; Wen Siyu; Lai Zhicen

doi:10.16383/j.aas.2017.e170103

忆阻递归神经网络稳定性分析及其在联想记忆中的应用

doi: 10.16383/j.aas.2017.e170103

鲍刚^1, ,,
陈媛媛^1,,
温思雨^1,,
赖陟岑^1,

基金项目:

Hubei Science and Technology Support Program 2015BAA106

Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control Program 2013KJX12

China Three Gorges University Science Foundation KJ2013B020

the National Natural Science Foundation of China 61125303

the Program for Changjiang Scholars and Innovative Research Team in University of China IRT1245

the Program for Science and Technology in Wuhan, China 2014010101010004

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- 被引次数: 0
出版历程
- 收稿日期: 2017-05-24
- 录用日期: 2017-10-12
- 刊出日期: 2017-12-20

Stability Analysis for Memristive Recurrent Neural Network and Its Application to Associative Memory

Bao Gang^{1
, ,},
Chen Yuanyuan^1
,,
Wen Siyu^1
,,
Lai Zhicen^1
,

1.
Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control, School of Electrical Engineering and New Energies, China Three Gorges University, Yichang 443002, China

Funds:

Hubei Science and Technology Support Program 2015BAA106

Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control Program 2013KJX12

China Three Gorges University Science Foundation KJ2013B020

the National Natural Science Foundation of China 61125303

the Program for Changjiang Scholars and Innovative Research Team in University of China IRT1245

the Program for Science and Technology in Wuhan, China 2014010101010004

More Information

Author Bio:
Yuanyuan Chen received the B.S.degree from the College of Science and Technology, China Three Gorges University in 2016.Now she is a postgraduate student and pursuing for M.S.degree at the School of Electrical Engineering and New Energies, China Three Gorges University.Her current research interests include microgrid optimization scheduling and stability analysis.E-mail:pretty.yuanzi@qq.com

Siyu Wen received the B.S.degree in water resources and hydropower engineering from the College of Science and Technology, China Three Gorges University in 2016.Now, she is currently working toward the M.S.degree at the School of Electrical Engineering and New Energies, China Three Gorges University.Her current research interests include hydropower dispatching and unit commitment optimization.E-mail:215341796@qq.com

Zhicen Lai received the B.S.degree in electrical engineering and its automation (focus on transmission line), China Three Gorges University in 2016.She is currently working toward the M.S.degree at the School of Electrical Engineering and New Energies, China Three Gorges University, Yichang, China.Her current research interests include microgrid control and stability analysis.E-mail:2512991452@qq.com

Corresponding author: Gang Bao 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.His research interests include memristor, stability analysis of nonlinear systems, and association memory.Corresponding author of this paper.E-mail:hustgangbao@ctgu.edu.cn

摘要

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Abstract: Memristor is a nonlinear resistor with variable resistance. This paper discusses dynamic properties of memristor and recurrent neural network (RNN) with memristors as connection weights. Firstly, it establishes that there exists a threshold voltage for memristor. Secondly, it presents a model for memristive recurrent neural network (MRNN) which has variable and bounded coe-cients, and analyzes stability of memristive neural network by some maths tools. Thirdly, it gives a synthesis algorithm for associative memory based on memristive recurrent neural network. At last, three examples verify our results.
- Associative memory /
- memristor /
- memristive recurrent neural network (MRNN) /
- stability
Recommended by Associate Editor Zhanshan Wang
注释:

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Fig. 1 The curve of $(v(t), i(t))$ under voltage sources with different amplitudes. The applied voltage source is $v(t)=v_0\sin(\omega t)$, $v_0=1.5, 1, 0.15, 0.01$ V, $\omega=2\pi$ rad/s and the other parameters are $s(t_0)=0.1$, $t_0=0$ s, $R_{\rm on}=100 \Omega $, $r=160$, $D=10^{-6} \mbox{cm}$, $\mu_V=10^{-10} \mbox{cm}^2/\mbox{sV}$. From four subplots, there is a threshold voltage existing for one memristor.

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Fig. 2 Transient behaviors of $x_{1}(t)$ of MRNN (26).

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Fig. 3 Transient behaviors of $x_{2}(t)$ of MRNN (26).

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Fig. 4 Phase plot of $x_{1}(t)$ and $x_{2}(t)$ of MRNN (26).

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Fig. 5 Transient behaviors of $x_{1}(t)$ and $x_{2}(t)$ of MRNN (27)

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Fig. 6 Three letters "I, L, U" and number "7" being presented by gray map.

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