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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Vydané v:	Neural processing letters Ročník 47; číslo 3; s. 949 - 973
Hlavný autor:	Popa, Călin-Adrian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.06.2018 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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