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Global Asymptotic Robust Stability and Global Exponential Robust Stability of Neural Networks with Time-Varying Delays

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Abstract

In this paper, based on nonnegative matrix theory, the Halanay’s inequality and Lyapunov functional, some novel sufficient conditions for global asymptotic robust stability and global exponential robust stability of neural networks with time-varying delays are presented. It is shown that our results improve and generalize several previous results derived in the literatures. From the obtained results, some linear matrix inequality criteria are derived. Finally, a simulation is given to show the effectiveness of the results.

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Authors and Affiliations

  1. School of Applied Mathematics, University of Electronic Science and Technology of China, 610054, Chengdu, Sichuan, People’s Republic of China

    Jin-Liang Shao, Ting-Zhu Huang & Sheng Zhou

Authors
  1. Jin-Liang Shao
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  2. Ting-Zhu Huang
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  3. Sheng Zhou
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Correspondence to Ting-Zhu Huang.

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Shao, JL., Huang, TZ. & Zhou, S. Global Asymptotic Robust Stability and Global Exponential Robust Stability of Neural Networks with Time-Varying Delays. Neural Process Lett 30, 229–241 (2009). https://doi.org/10.1007/s11063-009-9120-6

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