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Improved exponential stability criteria for recurrent neural networks with time-varying discrete and distributed delays

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Abstract

In this paper, the problem of the global exponential stability analysis is investigated for a class of recurrent neural networks (RNNs) with time-varying discrete and distributed delays. Due to a novel technique when estimating the upper bound of the derivative of Lyapunov functional, we establish new exponential stability criteria in terms of LMIs. It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to show the effectiveness of the proposed results.

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Author information

Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, PRC

    Yuan-Yuan Wu

  2. Department of Information and Communication, Nanjing University of Information Science and Technology, Nanjing, 210044, PRC

    Tao Li

  3. Institute of Automation, Qufu Normal University, Qufu, 273165, PRC

    Yu-Qiang Wu

Authors
  1. Yuan-Yuan Wu
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  2. Tao Li
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  3. Yu-Qiang Wu
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Corresponding author

Correspondence to Yu-Qiang Wu.

Additional information

This work was supported by National Natural Science Foundation of China (No. 60674027, No. 60974127) and Key Project of Education Ministry of China (No. 208074).

Yuan-Yuan Wu received her B. Sc. and M. Sc. degrees from the College of Mathematics and Information at Henan Normal University, Xinxiang, PRC in 2003 and 2006, respectively. She is now a Ph.D. candidate in School of Automation, Southeast University, Nanjing, PRC.

Her research interests include singular systems, delayed systems, and neural networks.

Tao Li received the Ph.D. degree from the School of Automation at Southeast University, Nanjing, PRC in 2008. He is now a lecturer in the Department of Information and Communication, Nanjing University of Information Science and Technology, Nanjing, PRC.

His research interests include neural networks and fault diagnosis.

Yu-Qiang Wu received the Ph.D. degree in automatic control from Southeast University, Nanjing, PRC in 1994. Currently, he is a professor in the Institute of Automation, Qufu Normal University, Qufu, PRC.

His research interests include variable structure control and nonlinear system control.

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Wu, YY., Li, T. & Wu, YQ. Improved exponential stability criteria for recurrent neural networks with time-varying discrete and distributed delays. Int. J. Autom. Comput. 7, 199–204 (2010). https://doi.org/10.1007/s11633-010-0199-z

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  • DOI: https://doi.org/10.1007/s11633-010-0199-z

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