State estimation for memristor-based neural networks with time-varying delays

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• 作者：Hongzhi Wei (1)
  Ruoxia Li (2)
  Chunrong Chen (1)
  
  1. Department of Mathematics and statistics ; Chongqing University ; Chongqing ; 401331 ; China
  2. Department of Applied Mathematics ; Yanshan University ; Qinhuangdao ; 066004 ; China
  
• 关键词：Memristor ; Neural network ; State estimation ; Global asymptotical stable ; Linear matrix inequalities
• 刊名：International Journal of Machine Learning and Cybernetics
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：6
• 期：2
• 页码：213-225
• 全文大小：737 KB
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• 刊物类别：Engineering
• 刊物主题：Artificial Intelligence and Robotics
  Statistical Physics, Dynamical Systems and Complexity
  Computational Intelligence
  Control , Robotics, Mechatronics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-808X

文摘

This paper is concerned with the state estimation problem for a class of memristor-based neural networks with time-varying delay. A delay dependent condition is developed to estimate the neuron states through observed output measurements such that the error system is globally asymptotically stable. By constructing more effective Lyapunov functionals, and combining with Jensen integral inequality and free-weighting matrix approach, a less conservative sufficient condition for the existence of state estimator is formulated in terms of linear matrix inequality, which can be checked efficiently by using some standard numerical packages. Finally, a numerical example is given to demonstrate the effectiveness of the presented results.