DocumentCode
807100
Title
Delay-dependent stability analysis for recurrent neural networks with time-varying delay
Author
Lu, C.-Y. ; Su, T.-J. ; Huang, S.-C.
Author_Institution
Dept. of Ind. Educ. & Technol., Nat. Changhua Univ. of Educ., Changhua
Volume
2
Issue
8
fYear
2008
Firstpage
736
Lastpage
742
Abstract
A global stability analysis of a particular class of recurrent neural networks with time-varying delay is performed. Both Lipschitz continuous and monotone non-decreasing activation functions are considered. Globally asymptotically delay-dependent stability criteria are derived in the form of linear matrix inequalities through the use of Leibniz-Newton formula and relaxation matrices. Finally, two numerical examples are given to illustrate the effectiveness of the given criterion.
Keywords
asymptotic stability; delays; linear matrix inequalities; neurocontrollers; recurrent neural nets; relaxation theory; time-varying systems; Lipschitz continuous activation functions; delay-dependent stability analysis; global stability analysis; linear matrix inequalities; monotone nondecreasing activation functions; recurrent neural networks; relaxation matrices; time-varying delay;
fLanguage
English
Journal_Title
Control Theory & Applications, IET
Publisher
iet
ISSN
1751-8644
Type
jour
DOI
10.1049/iet-cta:20070313
Filename
4567175
Link To Document