DocumentCode
1133915
Title
Absolute exponential stability of a class of continuous-time recurrent neural networks
Author
Hu, Sanqing ; Wang, Jun
Author_Institution
Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, China
Volume
14
Issue
1
fYear
2003
fDate
1/1/2003 12:00:00 AM
Firstpage
35
Lastpage
45
Abstract
This paper presents a new result on absolute exponential stability (AEST) of a class of continuous-time recurrent neural networks with locally Lipschitz continuous and monotone nondecreasing activation functions. The additively diagonally stable connection weight matrices are proven to be able to guarantee AEST of the neural networks. The AEST result extends and improves the existing absolute stability and AEST ones in the literature.
Keywords
asymptotic stability; matrix algebra; recurrent neural nets; H-matrix; absolute exponential stability; additively diagonally stable connection weight matrices; continuous-time recurrent neural networks; diagonal semistability; locally Lipschitz continuous functions; monotone nondecreasing activation functions; Automation; Councils; Eigenvalues and eigenfunctions; Neural networks; Neurons; Recurrent neural networks; Stability analysis; Sufficient conditions; Symmetric matrices;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/TNN.2002.806954
Filename
1176125
Link To Document