Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)

Convergence of Offline Gradient Method with Inner-penalty for Multi-output Feedforward Neural Networks

Authors
Zhou Fengqi, Liu Ergen, Xiao Yu
Corresponding Author
Zhou Fengqi
Available Online November 2013.
DOI
https://doi.org/10.2991/icmt-13.2013.30How to use a DOI?
Keywords
Feedforward neural networks; Offline gradient method; Inner-penalty; Convergence
Abstract
In this paper, we study an offline gradient method with inner-penalty for training multi-output feedforward neural networks. The monotonicity of the error function and weight boundedness for the offline gradient with inner-penalty are presented, both weak and strong convergence results are proved, which will be very meaningful for theoretical research or applications on multi-output neural networks.
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Proceedings
3rd International Conference on Multimedia Technology(ICMT-13)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2013
ISBN
978-90-78677-89-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/icmt-13.2013.30How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhou Fengqi
AU  - Liu Ergen
AU  - Xiao Yu
PY  - 2013/11
DA  - 2013/11
TI  - Convergence of Offline Gradient Method with Inner-penalty for Multi-output Feedforward Neural Networks
BT  - 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.30
DO  - https://doi.org/10.2991/icmt-13.2013.30
ID  - Fengqi2013/11
ER  -