Abstract
This paper studies learning algorithm of a dynamic binary neural network having rich dynamics. The algorithm is based on the genetic algorithm with an effective kernel chromosome and hidden neuron sharing. Performing basic numerical experiments, we have confirmed that the algorithm can store desired periodic teacher signals and the stored signals are stable for initial value.
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Ito, R., Nakayama, Y., Saito, T. (2011). Learning of Dynamic BNN toward Storing-and-Stabilizing Periodic Patterns. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_70
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DOI: https://doi.org/10.1007/978-3-642-24958-7_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24957-0
Online ISBN: 978-3-642-24958-7
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