Letters: New delay-variation-dependent stability for neural networks with time-varying delay

Authors:
Tao Li;Xin Yang;Pu Yang;Shumin Fei
Affiliations:
School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;School of Automation, Southeast University, Nanjing 210096, China
Venue:
Neurocomputing
Year:
2013

Citing 21
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Abstract

In this letter, together with some improved Lyapunov-Krasovskii functionals and effective mathematical techniques, several novel sufficient conditions are derived to guarantee a class of delayed neural networks (DNNs) to be asymptotically stable, in which both the time-delay and its time variation can be fully considered. Through combining reciprocal convex technique with earlier convex one, some previously ignored terms can be reconsidered and the stability criteria are presented in terms of LMIs, whose solvability heavily depends on the information on addressed DNNs as much as possible. Finally, it can be demonstrated by two numerical examples that our derived results reduce the conservatism more efficiently than some present ones with some comparing results.