The neurocomputing communities have focused much interest on quaternionic-valued
neural networks (QVNNs) due to the natural extension in quaternionic signals, learning of …

On real-world applications like image processing, speech reorganization, and signal
processing, QNN have significantly outperformed real-valued neural networks. This survey …

Quaternion neural networks have recently received an increasing interest due to noticeable
improvements over real-valued neural networks on real world tasks such as image, speech …

Abstract Machine Learning has recently emerged as a new paradigm for processing all
types of information. In particular, Artificial Intelligence is attractive to corporations & …

Quaternionic convolutional neural networks (QCNN) possess the ability to capture both
external dependencies between neighboring features and internal latent dependencies …

The nonlinear spatial grouping process of synapses is one of the fascinating methodologies
for neuro-computing researchers to achieve the computational power of a neuron …

In recent years, real-valued neural networks have demonstrated promising, and often
striking, results across a broad range of domains. This has driven a surge of applications …

Recent research indicates that complex-valued neural networks whose parameters (weights
and threshold values) are all complex numbers are in fact useful, containing characteristics …

This paper introduces a new quaternion multi-valued neural network architecture and
demonstrates its potential with numerical examples of multi-channel prediction and …

Complex-valued neural networks (CVNNs) deal with information in complex domain with
complex-valued parameters and variables. As explained in Section 2.2 in relation to …