Improved three-term conjugate gradient algorithm for training neural network
Author
Source
Journal of Kufa for Mathematics and Computer
Issue
Vol. 2, Issue 3 (30 Jun. 2015), pp.93-100, 8 p.
Publisher
University of Kufa Faculty of Mathematics and Computers Science
Publication Date
2015-06-30
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Topics
- Operations research
- Mathematical analysis
- Algorithms
- Equations
- Simulation methods
- Approximation theory
- Neural networks(Computer science)
Abstract EN
A new three-term conjugate gradient algorithm for training feed-forward neural networks is developed.
It is a vector based training algorithm derived from DFP quasi- Newton and has only O(n) memory.
The global convergence to the proposed algorithm has been established for convex function under Wolfe condition.
The results of numerical experiments are included and compared with other well known training algorithms in this field.
American Psychological Association (APA)
Taqi, Abbas Hasan. 2015. Improved three-term conjugate gradient algorithm for training neural network. Journal of Kufa for Mathematics and Computer،Vol. 2, no. 3, pp.93-100.
https://search.emarefa.net/detail/BIM-657007
Modern Language Association (MLA)
Taqi, Abbas Hasan. Improved three-term conjugate gradient algorithm for training neural network. Journal of Kufa for Mathematics and Computer Vol. 2, no. 3 (Jun. 2015), pp.93-100.
https://search.emarefa.net/detail/BIM-657007
American Medical Association (AMA)
Taqi, Abbas Hasan. Improved three-term conjugate gradient algorithm for training neural network. Journal of Kufa for Mathematics and Computer. 2015. Vol. 2, no. 3, pp.93-100.
https://search.emarefa.net/detail/BIM-657007
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-657007
