DocumentCode :
1367129
Title :
New stability criteria for Cohen-Grossberg neural networks with time delays
Author :
Hu, Lei ; Gao, Huijun ; Shi, Peng
Author_Institution :
Space Control & Inertial Technol. Res. Center, Harbin Inst. of Technol., Harbin, China
Volume :
3
Issue :
9
fYear :
2009
fDate :
9/1/2009 12:00:00 AM
Firstpage :
1275
Lastpage :
1282
Abstract :
The asymptotic stability is investigated for a class of time-delay Cohen-Grossberg neural networks, either with or without parameter uncertainties. By introducing a novel Lyapunov functional with the ideal of delay fractioning, a new criterion of asymptotic stability is derived in terms of a linear matrix inequality (LMI), which can be efficiently solved via standard numerical software. The criterion proves to be less conservative and the conservatism could be notably reduced by thinning the delay fractioning. Two examples are provided to demonstrate the less conservatism and effectiveness of the proposed stability conditions.
Keywords :
Lyapunov methods; asymptotic stability; delays; linear matrix inequalities; neurocontrollers; uncertain systems; Cohen-Grossberg neural network; Lyapunov function; asymptotic stability; linear matrix inequality; parameter uncertain system; time delay;
fLanguage :
English
Journal_Title :
Control Theory & Applications, IET
Publisher :
iet
ISSN :
1751-8644
Type :
jour
DOI :
10.1049/iet-cta.2008.0213
Filename :
5235429
Link To Document :
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