Design of Memristive Hopfield Neural Network using Memristor Bridges

  • Authors

    • Monurajan P
    • Ruhanbevi A
    • Manjula J
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16447
  • Bridge circuit, Hopfield neural network, Memristor.

  • Abstract

    Artificial Neural Networks are interconnection of neurons inspired from the biological neural network of the brain. ANN is claimed to rule the future, spreads its wings to various areas of interest to name a few such as optimization, information technology, cryptography, image processing and even in medical diagnosis. There are devices which possess synaptic behaviour, one such device is memristor. Bridge circuit of memristors can be combined together to form neurons. Neurons can be made into a network with appropriate parameters to store data or images. Hopfield neural networks are chosen to store the data in associative memory. Hopfield neural networks are a significant feature in ANN which are recurrent in nature and in general are used as associative memory and in solving optimization problems such as the Travelling Salesman Problem. The paper deals on the construction of memristive Hopï¬eld neural network using memristor bridging circuit and its application in the associative memory. This paper also illustrates the experiment with mathematical equations and the associative memory concept of the network using Matlab.

     

     

  • References

    1. [1] B. Yegnanarayana, “Artificial Neural Networksâ€, PHI Learning. ISBN9788120312531.URLhttps://books.google.co.in/books?id=RTtvUVU_xL4C, 2009.

      [2] Lidan Wang, Xiaodong Wang, Shukai Duan, and Huifang Li, “A spintronic memristor bridge synapse circuit and the application in memrisitive cellular automataâ€, Neurocomputing, 167(Supplement C):346 – 351, ISSN 0925-2312. doi: https://doi.org/10.1016/j.neucom.2015. 04.061, 2015.

      [3] L. Chua. Memristor-the missing circuit element. IEEE Transactions on Circuit Theory, 18(5):507–519, ISSN 0018-9324. doi: 10.1109/TCT.1971.1083337, September 1971.

      [4] Dmitri B. Strukov, Gregory S. Snider, Duncan R. Stewart, and R. Stanley Williams. The missing memristor found. 453: 80 EP , URL http://dx.doi.org/10.1038/ nature06932, May 2008.

      [5] Lidan Wang, Huifang Li, Shukai Duan, Tingwen Huang, and Huamin Wang. Pavlov associative memory in a memristive neural network and its circuit implementation. Neurocomputing, 171(Supplement C):23 – 29, ISSN 0925-2312. doi: https://doi.org/10.1016/j.neucom.2015. 05.078, 2016.

      [6] S. G. Hu, Y. Liu, Z. Liu, T. P. Chen, J. J. Wang, Q. Yu, L. J. Deng, Y. Yin, and Sumio Hosaka. Associative memory realized by a reconï¬gurable memristive hopï¬eld neural network. 6: 7522 EP –, Jun 2015. URL http://dx.doi.org/10.1038/ ncomms8522. Article.

      [7] Mikhail S. Tarkov, “Hopï¬eld Network with Interneuronal Connections Based on Memristor Bridges, pages 196–203. Springer International Publishing, Cham, ISBN 978-3-319-406633. doi: 10.1007/978-3-319-40663-3 23, 2016.

      [8] Hyongsuk Kim, Maheshwar P. Sah, Changju Yang, Tam´as Roska, and Leon O. Chua. “Memristor Bridge-Based Artiï¬cial Neural Weighting Circuitâ€, Springer International Publishing, Cham, ISBN 978-3-319-02630-5. doi: 10.1007/978-3-319-02630-5 12. pages 249–265. 2014.

      [9] Shukai Duan, Yi Zhang, Xiaofang Hu, Lidan Wang, and Chuandong Li. “Memristor-based chaotic neural networks for associative memoryâ€, Neural Computing and Applications, 25(6):1437–1445, ISSN 1433-3058. doi: 10. 1007/s00521-014-1633-x, Nov 2014.

      [10] Beiye Liu, Yiran Chen, Bryant Wysocki, and Tingwen Huang. â€Reconï¬gurable neuromorphic computing system with memristor-based synapse designâ€, Neural Processing Letters, 41(2):159–167, ISSN 1573-773X. doi: 10. 1007/s11063-013-9315-8, Apr 2015.

      [11] S. G. Hu, Y. Liu, Z. Liu, T. P. Chen, J. J. Wang, Q. Yu, L. J. Deng, Y. Yin, and Sumio Hosaka, “A memristive hopï¬eld network for associative memoryâ€, URL http://dx.doi.org/10.1038/protex.2015.070, July 2015.

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  • How to Cite

    P, M., A, R., & J, M. (2018). Design of Memristive Hopfield Neural Network using Memristor Bridges. International Journal of Engineering & Technology, 7(3.12), 652-655. https://doi.org/10.14419/ijet.v7i3.12.16447

    Received date: 2018-07-28

    Accepted date: 2018-07-28

    Published date: 2018-07-20