Quaternion-Valued Recurrent Projection Neural Networks on Unit Quaternions
Machine Learning
2020-09-14 v1 Neural and Evolutionary Computing
Machine Learning
Abstract
Hypercomplex-valued neural networks, including quaternion-valued neural networks, can treat multi-dimensional data as a single entity. In this paper, we present the quaternion-valued recurrent projection neural networks (QRPNNs). Briefly, QRPNNs are obtained by combining the non-local projection learning with the quaternion-valued recurrent correlation neural network (QRCNNs). We show that QRPNNs overcome the cross-talk problem of QRCNNs. Thus, they are appropriate to implement associative memories. Furthermore, computational experiments reveal that QRPNNs exhibit greater storage capacity and noise tolerance than their corresponding QRCNNs.
Keywords
Cite
@article{arxiv.2001.11846,
title = {Quaternion-Valued Recurrent Projection Neural Networks on Unit Quaternions},
author = {Marcos Eduardo Valle and Rodolfo Anibal Lobo},
journal= {arXiv preprint arXiv:2001.11846},
year = {2020}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1909.09227