An iterative learning scheme for multistate complex-valued and quaternionic hopfield neural networks

Authors:
Teijiro Isokawa;Haruhiko Nishimura;Nobuyuki Matsui
Affiliations:
University of Hyogo, Japan;University of Hyogo, Japan;University of Hyogo, Japan
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

We propose a learning scheme for multistate complex-valued and quaternionic neural networks in order to store correlated patterns with respect to each other. This is an extension of the so-called local iterative scheme for real-valued Hopfield neural networks. We first show the stability of desired memory patterns for a multistate complex-valued network and also for the multistate quaternionic network.