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2015 | OriginalPaper | Chapter

Simple Feature Quantities for Learning of Dynamic Binary Neural Networks

Authors : Ryuji Sato, Toshimichi Saito

Published in: Neural Information Processing

Publisher: Springer International Publishing

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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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Literature
1.
Kouzuki, R., Saito, T.: Learning of simple dynamic binary neural networks. IEICE Trans. Fundam. E96–A(8), 1775–1782 (2013)CrossRef
2.
Moriyasu, J., Saito, T.: Sparsification and stability of simple dynamic binary neural networks. IEICE Trans. Fundam. E97–A(4), 985–988 (2014)CrossRef
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) CrossRef
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)
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)
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)CrossRef
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)CrossRef
8.
Chua, L.O.: A Nonlinear Dynamics Perspective of Wolfram’s New Kind of Science, I and II. World Scientific, Singapore (2005)
9.
Wada, W., Kuroiwa, J., Nara, S.: Completely reproducible description of digital sound data with cellular automata. Phys. Lett. A 306, 110–115 (2002)MathSciNetCrossRefMATH
10.
Rosin, P.L.: Training cellular automata for image processing. IEEE Trans. Image Process. 15(7), 2076–2087 (2006)CrossRef
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)CrossRef
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)CrossRef
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)
14.
Araki, K., Saito, T.: An associative memory including time-variant self-feedback. Neural Netw. 7(8), 1267–1271 (1994)CrossRef
Metadata
Title
Simple Feature Quantities for Learning of Dynamic Binary Neural Networks
Authors
Ryuji Sato
Toshimichi Saito
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26531-5

Electronic ISBN: 978-3-319-26532-2

Copyright Year: 2015

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

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