Improved exponential stability analysis for delayed recurrent neural networks |
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| Authors: | Magdi S Mahmoud Yuanqing Xia |
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| Institution: | a Systems Engineering Department, King Fahd University of Petroleum and Minerals, P.O. Box 985, Dhahran 31261, Saudi Arabia
b Department of Automatic Control, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | In this paper, we investigate the problem of global exponential stability analysis for a class of delayed recurrent neural networks. This class includes Hopfield neural networks and cellular neural networks with interval time-delays. Improved exponential stability condition is derived by employing new Lyapunov-Krasovskii functional and the integral inequality. The developed stability criteria are delay dependent and characterized by linear matrix inequalities (LMIs). The developed results are less conservative than previous published ones in the literature, which are illustrated by representative numerical examples. |
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