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文章基本信息

  • 标题:Delay-Dependent Criterion for Exponential Stability Analysis of Neural Networks with Time-Varying Delays
  • 本地全文:下载
  • 作者:Arash Farnam ; Reza Mahboobi Esfanjani ; Aghil Ahmadi
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2016
  • 卷号:49
  • 期号:10
  • 页码:130-135
  • DOI:10.1016/j.ifacol.2016.07.501
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThis note investigates the problem of exponential stability of neural networks with time-varying delays. To derive a less conservative stability condition, a novel augmented Lyapunov-Krasovskii functional (LKF) which includes triple and quadruple-integral terms is employed. In order to reduce the complexity of the stability test, the convex combination method is utilized to derive an improved delay-dependent stability criterion in the form of linear matrix inequalities (LMIs). The superiority of the proposed approach is demonstrated by two comparative examples.
  • 关键词:KeywordsExponential StabilityNeural NetworksTime-Varying DelayLinear Matrix Inequality.
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