DocumentCode
393468
Title
Multi-branch structure of layered neural networks
Author
Yamashita, T. ; Hirasawa, K. ; Hu, J. ; Murata, J.
Author_Institution
Graduate Sch. of Inf. Sci & Electr. Eng.., Kyushu Univ., Japan
Volume
2
fYear
2002
fDate
5-7 Aug. 2002
Firstpage
759
Abstract
In this paper, a multi-branch structure of neural networks is studied to make their size compact. The multi-branch structure has shown improved performance against conventional neural networks. As a result, it has been proved that the number of nodes of networks and the computational cost for training networks can be reduced.
Keywords
learning (artificial intelligence); multilayer perceptrons; neural nets; Universal Learning Networks; layered neural networks; multi-branch structure; neural networks; universal learning; Computational efficiency; Delay effects; Differential equations; Gradient methods; Information science; Input variables; Neural networks; Nonlinear equations; Sampling methods;
fLanguage
English
Publisher
ieee
Conference_Titel
SICE 2002. Proceedings of the 41st SICE Annual Conference
Print_ISBN
0-7803-7631-5
Type
conf
DOI
10.1109/SICE.2002.1195252
Filename
1195252
Link To Document