Abstract
This paper studies the deep dynamic binary neural network that is characterized by the signum activation function, ternary weighting parameters and integer threshold parameters. In order to store a desired binary periodic orbit, we present a simple learning method based on the correlation learning. The method is applied to a teacher signal that corresponds to control signal of the matrix converter in power electronics. Performing numerical experiments, we investigate storage of the teacher signal and its stability as the depth of the network varies.
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Moriyasu, J., Saito, T. (2014). A Deep Dynamic Binary Neural Network and Its Application to Matrix Converters. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_77
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DOI: https://doi.org/10.1007/978-3-319-11179-7_77
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
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