DocumentCode
1996997
Title
The Research on the GC Property for RNNs with Limited Matrix 2-Norm
Author
Chen Qiao ; Rui Zhang ; Jing Yao ; Xiangliang Kong ; Changsheng Zhou
Author_Institution
Sch. of Math. & Stat., Xi´an Jiaotong Univ., Xi´an, China
fYear
2013
fDate
3-4 Dec. 2013
Firstpage
82
Lastpage
86
Abstract
The global convergence (GC) analysis of recurrent neural networks (RNNs) is a first and necessary step for any practical applications of them. In the present paper, when the connecting matrix of the RNNs with projection mapping owning limited norm, the GC property is assured under the critical condition. The results given here not only improve deeply upon the existing relevant critical as well as non-critical dynamics conclusions in literature, but also can be used in the practical application of RNNs directly.
Keywords
convergence; matrix algebra; recurrent neural nets; GC property; RNNs; global convergence analysis; limited matrix 2-norm; recurrent neural networks; Analytical models; Biological neural networks; Convergence; Educational institutions; Recurrent neural networks; global convergence; matrix 2-norm; projection mapping; recurrent neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems (GCIS), 2013 Fourth Global Congress on
Conference_Location
Hong Kong
Print_ISBN
978-1-4799-2885-9
Type
conf
DOI
10.1109/GCIS.2013.19
Filename
6805916
Link To Document