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Neural Computing and Applications

Erschienen in:

01.05.2013 | Original Article

Feed-back neural networks with discrete weights

verfasst von: Lijuan Wang, Qingguo Zhou, Tao Jin, Hong Zhao

Erschienen in: Neural Computing and Applications | Ausgabe 6/2013

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Abstract

We use the Monte Carlo Adaptation learning algorithm to design feed-back neural networks with discrete weights. The dynamic properties of these types of neural networks are investigated as a function of the states of weights. The numerical results of these networks show three phases: the “chaos phase,” the “pure memory phase” and the “mixture phase” in the parameter space. The maximum storage ratio for the “pure memory phase” increases with the increasing of the states of the weights, which is favorable for practical applications.

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Titel: Feed-back neural networks with discrete weights
verfasst von: Lijuan Wang
Qingguo Zhou
Tao Jin
Hong Zhao
Publikationsdatum: 01.05.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 6/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0867-8

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