Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 19, Issue 12, Pages 2154-2161Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2008.2006904
Keywords
Asymptotic stability; delay-dependent criteria; linear matrix inequality (LMI); neural networks; uncertain delay
Categories
Funding
- National Natural Science Foundation of China [60864002]
- Australian Research Council
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This brief is concerned with asymptotic stability of neural networks with uncertain delays. Two types of uncertain delays are considered: one is constant while the other is time varying. The discretized Lyapunov-Krasovskii functional (LKF) method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz-Newton formula. The integrated method leads to the establishment of new delay-dependent sufficient conditions in form of linear matrix inequalities for asymptotic stability of delayed neural networks (DNNs). A numerical simulation study is conducted to demonstrate the obtained theoretical results, which shows their less conservatism than the existing stability criteria.
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