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Pseudomemories of two‐dimensional multistate hopfield neural networks
Author(s) -
Kobayashi Masaki
Publication year - 2017
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22374
Subject(s) - artificial neural network , computer science , hopfield network , noise (video) , artificial intelligence , scale (ratio) , algorithm , pattern recognition (psychology) , cartography , image (mathematics) , geography
Complex‐valued Hopfield neural networks (CVHNNs) are available for storage of multilevel data, such as gray‐scale images. Such networks have low noise tolerance. This is a severe problem for their applications. To improve the noise tolerance, we have to study pseudomemories. In the case of one training pattern, CVHNNs have only rotated patterns as pseudomemories. There are many rotated patterns. This is considered the reason why CVHNNs have low noise tolerance. In the present paper, we investigate the pseudomemories of two‐dimensional multistate Hopfield neural networks, including the complex‐valued ones, with multiple training patterns. Computer simulations show that there are many pseudomemories other than the rotated patterns. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.