Journal
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
Volume 355, Issue 13, Pages 5394-5413Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jfranklin.2018.05.037
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Funding
- National Natural Science Foundation of China [11301308, 61673247]
- Research Fund for Distinguished Young Scholars and Excellent Young Scholars of Shandong Province [JQ201719, ZR2016JL024]
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In this paper, we investigate the problem of finite-time stabilization of time-varying delayed neural networks with uncertainty. By employing the Lyapunov approach and linear matrix inequalities (LMIs), two different memory controllers are derived to achieve the finite-time stabilization of the addressed neural networks. Moreover, the upper bound of the setting-time for stabilization can be estimated via different Lyapunov functions. Our results improve and extend some recent works. Finally, the effectiveness and feasibility of the proposed controllers are demonstrated by numerical simulations. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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