DocumentCode
3280945
Title
Bifurcations in the learning of recurrent neural networks
Author
Doya, Kenji
Author_Institution
Dept. of Biol., California Univ., San Diego, CA, USA
Volume
6
fYear
1992
fDate
10-13 May 1992
Firstpage
2777
Abstract
Gradient descent algorithms in recurrent neural networks can have problems when the network dynamics experience bifurcations in the course of learning. The possible hazards caused by the bifurcations of the network dynamics and the learning equations are investigated. The roles of teacher forcing, preprogramming of network structures, and the approximate learning algorithms are discussed
Keywords
bifurcation; learning (artificial intelligence); recurrent neural nets; bifurcations; learning; learning algorithms; learning equations; network dynamics; network structures; preprogramming; recurrent neural networks; teacher forcing; Bifurcation; Biological neural networks; Biological system modeling; Equations; Feedforward systems; Hazards; Intelligent networks; Recurrent neural networks; Speech recognition; Supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location
San Diego, CA
Print_ISBN
0-7803-0593-0
Type
conf
DOI
10.1109/ISCAS.1992.230622
Filename
230622
Link To Document