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Passivity analysis of stochastic delayed neural networks with Markovian switching

机译:马尔可夫切换的随机延迟神经网络的无源性分析

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摘要

In this paper, the problem of passivity analysis is investigated for a class of stochastic delayed neural networks with Markovian switching. By applying Lyapunov functional and free-weighting matrix, delay-dependent/independent passivity criteria are presented in terms of linear matrix inequalities. The results herein include existing ones for neural networks without Markovian switching as special cases. An example is given to demonstrate the effectiveness of the proposed criteria.
机译:本文研究了一类带马尔可夫切换的随机时滞神经网络的无源性分析问题。通过应用李雅普诺夫泛函和自由加权矩阵,根据线性矩阵不等式提出了依赖于延迟/独立于被动的准则。这里的结果包括没有特殊情况的马尔可夫切换的现有神经网络结果。举例说明了所提出标准的有效性。

著录项

  • 来源
    《Neurocomputing》 |2011年第10期|p.1754-1761|共8页
  • 作者

    SongZhu; Yi Shen;

  • 作者单位

    College of Sciences, China University of Mining and Technology, Xuzhou 221116, China,Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;

    Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;

  • 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

    stochastic neural networks; linear matrix inequality; passivity; markov chain;

    机译:随机神经网络;线性矩阵不等式;被动性;马尔可夫链;

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