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Simple Feature Quantities for Learning of Dynamic Binary Neural Networks

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

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Abstract

This paper presents simple feature quantities for learning of dynamic binary neural networks. The teacher signal is a binary periodic orbit corresponding to control signal of switching circuits. The feature quantities characterize generation of spurious memories and stability of the teacher signal. We present a simple greedy search based algorithm where the two feature quantities are used as cost functions. Performing basic numerical experiments, the algorithm efficiency is confirmed.

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References

  1. Kouzuki, R., Saito, T.: Learning of simple dynamic binary neural networks. IEICE Trans. Fundam. E96–A(8), 1775–1782 (2013)

    Article  Google Scholar 

  2. Moriyasu, J., Saito, T.: Sparsification and stability of simple dynamic binary neural networks. IEICE Trans. Fundam. E97–A(4), 985–988 (2014)

    Article  Google Scholar 

  3. Nakayama, Y., Kouzuki, R., Saito, T.: Application of the dynamic binary neural network to switching circuits. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 697–704. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Moriyasu, J., Saito, T.: A deep dynamic binary neural network and its application to matrix converters. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 611–618. Springer, Heidelberg (2014)

    Google Scholar 

  5. Moriyasu, J., Saito, T.: A cascade system of simple dynamic binary neural networks and its sparsification. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part I. LNCS, vol. 8834, pp. 231–238. Springer, Heidelberg (2014)

    Google Scholar 

  6. Gray, D.L., Michel, A.N.: A training algorithm for binary feed forward neural networks. IEEE Trans. Neural Netw. 3(2), 176–194 (1992)

    Article  Google Scholar 

  7. Chen, F., Chen, G., He, Q., He, G., Xu, X.: Universal perceptron and DNA-like learning algorithm for binary neural networks: non-LSBF implementation. IEEE Trans. Neural Netw. 20(8), 1293–1301 (2009)

    Article  Google Scholar 

  8. Chua, L.O.: A Nonlinear Dynamics Perspective of Wolfram’s New Kind of Science, I and II. World Scientific, Singapore (2005)

    Google Scholar 

  9. Wada, W., Kuroiwa, J., Nara, S.: Completely reproducible description of digital sound data with cellular automata. Phys. Lett. A 306, 110–115 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  10. Rosin, P.L.: Training cellular automata for image processing. IEEE Trans. Image Process. 15(7), 2076–2087 (2006)

    Article  Google Scholar 

  11. Iguchi, T., Hirata, A., Torikai, H.: Theoretical and heuristic synthesis of digital spiking neurons for spike-pattern-division multiplexing. IEICE Trans. Fundam. E93–A(8), 1486–1496 (2010)

    Article  Google Scholar 

  12. Bose, B.K.: Neural network applications in power electronics and motor drives - an introduction and perspective. IEEE Trans. Ind. Electron. 54(1), 14–33 (2007)

    Article  Google Scholar 

  13. Rodriguez, J., Rivera, M., Kolar, J.W., Wheeler, P.W.: A review of control and modulation methods for matrix converters. ieee tie 59(1), 58–70 (2012)

    Google Scholar 

  14. Araki, K., Saito, T.: An associative memory including time-variant self-feedback. Neural Netw. 7(8), 1267–1271 (1994)

    Article  Google Scholar 

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Authors and Affiliations

  1. Hosei University, Koganei, Tokyo, 184-8584, Japan

    Ryuji Sato & Toshimichi Saito

Authors
  1. Ryuji Sato
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  2. Toshimichi Saito
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Corresponding author

Correspondence to Toshimichi Saito .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Sato, R., Saito, T. (2015). Simple Feature Quantities for Learning of Dynamic Binary Neural Networks. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_25

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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