Global Robust Exponential Stability for Interval Delayed Neural Networks with Possibly Unbounded Activation Functions

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• 作者：Sitian Qin (1)
  Dejun Fan (1)
  Ming Yan (1)
  Qinghe Liu (2)
  
• 关键词：Delayed neural networks with possibly unbounded activation ; Global robust exponential stability ; Topological degree theory
• 刊名：Neural Processing Letters
• 出版年：2014
• 出版时间：August 2014
• 年：2014
• 卷：40
• 期：1
• 页码：35-50
• 全文大小：368 KB
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• 作者单位：Sitian Qin (1)
  Dejun Fan (1)
  Ming Yan (1)
  Qinghe Liu (2)
  
  1. Department of Mathematics, Harbin Institute of Technology at Weihai, Weihai, 264209, People鈥檚 Republic of China
  2. Automotive School, Harbin Institute of Technology at Weihai, Weihai, 264209, People鈥檚 Republic of China
  
• ISSN：1573-773X

文摘

In this paper, we mainly study the global robust exponential stability of the neural networks with possibly unbounded activation functions. Based on the topological degree theory and Lyapunov functional method, we provide some new sufficient conditions for the global robust exponential stability. Under these conditions, we prove existence, uniqueness and global robust exponential stability of equilibrium point. In the end, some examples are provided to demonstrate the validity of the theoretical results.