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Introducing quaternion multi-valued neural networks with numerical examples.
- Source :
-
Information Sciences . Jan2018, Vol. 423, p326-342. 17p. - Publication Year :
- 2018
-
Abstract
- This paper introduces a new quaternion multi-valued neural network architecture and demonstrates its potential with numerical examples of multi-channel prediction and classification. A variety of real-valued learning structures have been introduced in prior literature; an important example is the multilayer perceptron neural network, which forms the underlying basis for modern deep learning architectures. However, in multidimensional information processing problems, real-valued learning structures perform suboptimally due to distortion of inter-channel relationships. A natural way to represent multidimensional data is using quaternions, a four-dimensional associative normed division algebra over the real numbers that allows for the multiplication and division of points in three-dimensional space. This paper introduces quaternion multi-valued neural networks, which perform nonlinear operations on the three-dimensional phase of quaternion data points. As shown with two numerical examples, the proposed quaternion multi-valued neural network outperforms existing learning structures, particularly in cases where limited training data is available. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 423
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 125722745
- Full Text :
- https://doi.org/10.1016/j.ins.2017.09.057