An Introduction to Quaternion-Valued Recurrent Projection Neural Networks

Abstract

Hypercomplex-valued neural networks, including quaternion-valued neural networks, can treat multi-dimensional data as a single entity. In this paper, we introduce 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.

arXiv commentAccepted to be Published in: Proceedings of the 8th Brazilian Conference on Intelligent Systems (BRACIS 2019), October 15-18, 2019, Salvador, BA, Brazil

References in corpus (2)

• On the Dynamics of Hopfield Neural Networks on Unit Quaternions
• A Broad Class of Discrete-Time Hypercomplex-Valued Hopfield Neural Networks

Cited by in corpus (2)

• Hypercomplex-Valued Recurrent Correlation Neural Networks
• Quaternion-Valued Recurrent Projection Neural Networks on Unit Quaternions