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A Flexible Memristor-Based Neural Network

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 951))

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Abstract

Many memristor-based neural network arrays that have been proposed in recent years are simultaneously dealt with all of their signal inputs in signal reception status. Therefore, when a relatively small-scale neural network is implemented with this memristive array, some of the inputs which are not used may cause errors in the result due to the impact of an unexpected signal. In this paper, a flexible memristor-based neural network is proposed. Based on this network, the number of synapses used at work can be flexibly configured according to the required size, thereby improving system performance. The memristor-based neural network is simulated in Pspice to implement two different scales, which proves the feasibility and effectiveness of a flexible memristive neural network.

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Acknowledgments

The work is supported by the State Key Program of National Natural Science of China (Grant No. 61632002), the National Key R&D Program of China for International S&T Cooperation Projects (No. 2017YFE010 3900), the National Natural Science of China (Grant Nos. 61603348, 61775198, 61603347, 61572446, 61472372), Science and Technology Innovation Talents Henan Province (Grant No. 174200510012), Research Program of Henan Province (Grant Nos. 172102210066, 17A120005, 182102210160), Youth Talent Lifting Project of Henan Province and the Science Foundation of for Doctorate Research of Zhengzhou University of Light Industry (Grant No. 2014BSJJ044).

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Authors and Affiliations

  1. Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Junwei Sun, Gaoyong Han & Yanfeng Wang

  2. School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Junwei Sun, Gaoyong Han & Yanfeng Wang

Authors
  1. Junwei Sun
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  2. Gaoyong Han
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  3. Yanfeng Wang
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Corresponding author

Correspondence to Yanfeng Wang .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Sun, J., Han, G., Wang, Y. (2018). A Flexible Memristor-Based Neural Network. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_23

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2825-1

  • Online ISBN: 978-981-13-2826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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