New results for global exponential stability of neural networks with varying delays

Authors:
Yajuan Liu;Wanbiao Ma;Magdi S. Mahmoud
Affiliations:
Department of Applied Mathematics, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China;Department of Applied Mathematics, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China;Systems Engineering Department, King Fahd University of Petroleum and Minerals, P.O. Box 5067, Dhahran 31261, Saudi Arabia
Venue:
Neurocomputing
Year:
2012

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Abstract

In this paper, the problem of global exponential stability for a class of neural networks with interval time-varying delay is investigated. The time-delay pattern is quite general and including fast time-varyings. It is assumed that the time delay belongs to a given interval, but the derivative of a time-varying delay be less than 1 is removed, or the delay function is not necessary to be differentiable. By constructing a set of improved Lyapunov-Krasovskii functionals combined with a known integral inequality, new delay-dependent exponential stability criteria with explicitly exponential convergence rate are established in terms of LMIs (linear matrix inequalities). The stability criteria are less conservative than the existing results in the literatures. Numerical examples are given to illustrate the effectiveness of the results.