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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Neural processing letters Ročník 47; číslo 3; s. 949 - 973
Hlavní autor: Popa, Călin-Adrian
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2018
Springer Nature B.V
Témata:
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 přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 the real- and complex-valued cases led to the idea of extending them to the quaternion domain, also. Experiments done using the proposed training methods on time series prediction applications showed a significant performance improvement over the quaternion gradient descent algorithm.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-017-9716-1