Attractors accompanied with a training pattern of multivalued hopfield neural networks
Masaki Kobayashi · IEEJ Transactions on Electrical and Electronic Engineering · 2014
Recently, multivalued Hopfield neural networks, such as complex‐valued Hopfield neural networks, and their applications have been studied by many researchers. In their application, low noise robustness is an important problem. Too many attractors accompanied with training patterns damage the noise robustness. Rotated patterns are well‐known attractors of complex‐valued Hopfield neural networks. In the present work, we reveal that any other patterns are never attractors in complex‐valued Hopfield neural networks with a training pattern. We also prove a similar result for rotor Hopfield neural network. In addition, we investigate the relation between noise robustness and the number of attractors. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.