Improved exponential stability analysis for delayed recurrent neural networks

Magdi S. Mahmoud; Yuanqing Xia

doi:10.1016/j.jfranklin.2010.11.002

Improved exponential stability analysis for delayed recurrent neural networks

Magdi S. Mahmoud, Yuanqing Xia

School of Automation

King Fahd University of Petroleum and Minerals

Research output: Contribution to journal › Article › peer-review

46 Citations (Scopus)

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 LyapunovKrasovskii 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.

Original language	English
Pages (from-to)	201-211
Number of pages	11
Journal	Journal of the Franklin Institute
Volume	348
Issue number	2
DOIs	https://doi.org/10.1016/j.jfranklin.2010.11.002
Publication status	Published - Mar 2011

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Mahmoud, M. S., & Xia, Y. (2011). Improved exponential stability analysis for delayed recurrent neural networks. Journal of the Franklin Institute, 348(2), 201-211. https://doi.org/10.1016/j.jfranklin.2010.11.002

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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 LyapunovKrasovskii 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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AB - 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 LyapunovKrasovskii 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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Improved exponential stability analysis for delayed recurrent neural networks

Abstract

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