Learning Algorithms for Quaternion-Valued Neural Networks

This paper presents the deduction of the enhanced gradient descent, conjugate gradient, scaled conjugate gradient, quasi-Newton, and Levenberg–Marquardt methods for training quaternion-valued feedforward neural networks, using the framework of the HR calculus. The performances of these algorithms in...

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Bibliographic Details
Published in:	Neural processing letters Vol. 47; no. 3; pp. 949 - 973
Main Author:	Popa, Călin-Adrian
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2018 Springer Nature B.V
Subjects:	Algorithms Approximation Artificial Intelligence Artificial neural networks Back propagation Calculus Complex Systems Computational Intelligence Computer Science Conjugate gradient method Machine learning Neural networks Quaternions Teaching methods Time series Quaternion-valued neural networks Resilient backpropagation SuperSAB Levenberg–Marquardt algorithm Conjugate gradient algorithms Quickprop Scaled conjugate gradient algorithm Time series prediction Quasi-Newton algorithms Delta-bar-delta
ISSN:	1370-4621, 1573-773X
Online Access:	Get full text
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