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
fDate :
9/1/2009 12:00:00 AM
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;
Journal_Title :
Control Theory & Applications, IET
DOI :
10.1049/iet-cta.2008.0213