An iterative learning scheme for multistate complex-valued and quaternionic Hopfield neural networks
Teijiro Isokawa, H. Nishimura, Nobuyuki Matsui · 2009
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.