Finite-time stability criteria for a class of fractional-order neural networks with delay

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• 作者：Liping Chen ; Cong Liu ; Ranchao Wu ; Yigang He ; Yi Chai
• 关键词：Finite ; time stability ; Fractional order ; Neural network ; Delayed systems
• 刊名：Neural Computing & Applications
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：27
• 期：3
• 页码：549-556
• 全文大小：528 KB
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• 作者单位：Liping Chen (1)
  Cong Liu (1)
  Ranchao Wu (2)
  Yigang He (1)
  Yi Chai (3)
  
  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China
  2. School of Mathematics, Anhui University, Hefei, 230039, China
  3. School of Automation, Chongqing University, Chongqing, 400044, China
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

Finite-time stabilities of a class of fractional-order neural networks delayed systems with order \(\alpha {:}\) \(0<\alpha \le 0.5\) and \(0.5<\alpha <1\) are addressed in this paper, respectively. By using inequality technique, two new delay-dependent sufficient conditions ensuring stability of such fractional-order neural networks over a finite-time interval are obtained. Obtained conditions are less conservative than that given in the earlier references. Two numerical examples are given to show the effectiveness of our proposed method.