Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 19, Issue 5, Pages 855-873Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2007.912319
Keywords
recurrent neural networks; global asymptotic stability; multiple time-varying delays; linear matrix inequality (LMI); Lyapunov-Krasovskii functional
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Funding
- National Natural Science Foundation of China [60534010, 60572070, 60521003, 60774048, 60728307]
- National High Technology Research and Development Program of China [2006AA04ZI83]
- Program for Changjiang Scholars and Innovative Research Groups of China [60521003]
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In this paper, several sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time-varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed in our investigation. The results are shown to be generalizations of some previously published results and are less conservative than existing results. The present results are also applied to recurrent neural networks with constant time delays.
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