DocumentCode
389274
Title
Learning algorithm for neural network with alternating process
Author
Liu, Wei-Guo ; Yong, Lin ; Huang, Yong-xuan
Author_Institution
Syst. Eng. Inst., Xi´´an Jiaotong Univ., China
Volume
2
fYear
2002
fDate
2002
Firstpage
760
Abstract
This paper is concerned with improving the learning efficiency and stability of neural networks. An alternating algorithm is presented for the training process. The method is divided into two stages. First, the gradient method is applied, and then a bisection technique is used. The convergence is proved for the proposed method, so that a stable distribution of weights can be reached. Compared with the standard gradient method, the oscillating divergent phenomenon is avoided.
Keywords
convergence; feedforward neural nets; gradient methods; learning (artificial intelligence); stability; alternating algorithm; bisection technique; convergence; feedforward neural network; gradient method; learning efficiency; oscillating divergent phenomenon avoidance; stability; stable weight distribution; training process; Artificial neural networks; Convergence; Educational institutions; Electronic mail; Feedforward neural networks; Feeds; Gradient methods; Neural networks; Signal processing algorithms; Systems engineering and theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN
0-7803-7508-4
Type
conf
DOI
10.1109/ICMLC.2002.1174482
Filename
1174482
Link To Document