Convergence of Offline Gradient Method with Inner-penalty for Multi-output Feedforward Neural Networks
Zhou Fengqi, Liu Ergen, Xiao Yu
Available Online November 2013.
- https://doi.org/10.2991/icmt-13.2013.30How to use a DOI?
- Feedforward neural networks; Offline gradient method; Inner-penalty; Convergence
- 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.
- 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 -