Title :
An iterative learning scheme for multistate complex-valued and quaternionic Hopfield neural networks
Author :
Isokawa, Teijiro ; Nishimura, Haruhiko ; Matsui, Nobuyuki
Author_Institution :
Univ. of Hyogo, Kobe, Japan
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.
Keywords :
Hopfield neural nets; learning (artificial intelligence); Hopfield neural networks; correlated patterns; iterative learning scheme; memory patterns; multistate complex-valued network; multistate quaternionic network; Associative memory; Computer graphics; Helium; Hopfield neural networks; Mathematics; Neural networks; Neurons; Physics; Quaternions; Stability;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5178996
