DocumentCode
2177479
Title
Fast learning algorithms for multi-layered feedforward neural network
Author
Min Liang ; Wang, Shi-Xi ; Luo, Young-Hong
Author_Institution
Center for Studying & Training of Med. Apparatus, Hunan Med. Univ., Changsha, China
fYear
1994
fDate
23-27 May 1994
Firstpage
787
Abstract
In this paper, the problem of fast learning algorithm for multi-layered feedforward neural network (MLFNN) is discussed. A new fast backpropagation (FB-P) learning algorithm is proposed, By the analysis of FB-P learning algorithm, a modified FB-P (MFB-P) learning algorithm is presented. Simulations are run with the problem of XOR for B-P, FB-P and MFB-P, and the corresponding results indicate that MFB-P or FB-P converges much more quickly than B-P and MFB-P has much better generalization than FB-P or B-P
Keywords
backpropagation; feedforward neural nets; fast backpropagation learning algorithm; learning algorithm; learning algorithms; multilayered feedforward neural network; simulation; Acceleration; Algorithm design and analysis; Biomedical engineering; Convergence; Feedforward neural networks; Multi-layer neural network; Neural networks; Paper technology; Pattern analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Aerospace and Electronics Conference, 1994. NAECON 1994., Proceedings of the IEEE 1994 National
Conference_Location
Dayton, OH
Print_ISBN
0-7803-1893-5
Type
conf
DOI
10.1109/NAECON.1994.332959
Filename
332959
Link To Document