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Robust exponential stability criterion for uncertain neural networks with discontinuous activation functions and time-varying delays

机译:具有不连续激活函数和时变时滞的不确定神经网络的鲁棒指数稳定性判据

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摘要

This paper considers the global robust exponential stability of time-varying delayed neural networks with discontinuous activation functions and norm-bounded uncertainties. Based on the Lyapunov-Krasovskii stability theory, we originally analyze the global robust exponential stability of discontinuous neural networks with time-varying delays in view of the linear matrix inequalities (LMIs). Therefore, our results are brand new compared to previous literatures. A numerical example is given to validate the effectiveness of our results.
机译:本文考虑具有不连续激活函数和范数界不确定性的时变时滞神经网络的全局鲁棒指数稳定性。基于Lyapunov-Krasovskii稳定性理论,我们最初根据线性矩阵不等式(LMI)分析具有时变时滞的不连续神经网络的全局鲁棒指数稳定性。因此,与以前的文献相比,我们的结果是全新的。数值例子验证了我们结果的有效性。

著录项

  • 来源
    《Neurocomputing》 |2010年第9期|p.1265-1271|共7页
  • 作者单位

    College of Electric and Information Technology, Hunan University, Changsha, Hunan 410082, PR China;

    College of Electric and Information Technology, Hunan University, Changsha, Hunan 410082, PR China;

    rnCollege of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, PR China;

    rnSchool of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, Hunan 410004, PR ChinarnThe ARIS Lab, School of Engineering, University of Guelph, Cuelph, Ontario, Canada N2L 2W1;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    delayed neural networks; global robust exponential stability; LMIs; discontinuous activation functions; norm-bounded uncertainties;

    机译:延迟神经网络;全局鲁棒指数稳定性;LMI;不连续的激活功能;规范约束的不确定性;

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