Finite-Time Anti-synchronization Control of Memristive Neural Networks With Stochastic Perturbations

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• 作者：Weiping Wang ; Lixiang Li ; Haipeng Peng ; Jürgen Kurths…
• 关键词：Memristive neural networks ; Finite ; time synchronization ; Anti ; synchronization
• 刊名：Neural Processing Letters
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：43
• 期：1
• 页码：49-63
• 全文大小：614 KB
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• 作者单位：Weiping Wang (1)
  Lixiang Li (2)
  Haipeng Peng (2)
  Jürgen Kurths (3)
  Jinghua Xiao (1)
  Yixian Yang (2) (4)
  
  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
  2. Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
  3. Potsdam Institute for Climate Impact Research, 14473, Potsdam, Germany
  4. National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing, 100876, China
  
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Complexity
  Artificial Intelligence and Robotics
  Electronic and Computer Engineering
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1573-773X

文摘

In this paper, finite-time anti-synchronization control of memristive neural networks with stochastic perturbations is studied. We investigate a class of memristive neural networks with two different types of memductance functions. The purpose of the addressed problem is to design a nonlinear controller which can obtain anti-synchronization of the drive system and the response system in finite time. Based on two kinds of memductance functions, finite-time stability criteria are obtained for memristive neural networks with stochastic perturbations. The analysis in this paper employs differential inclusions theory, finite-time stability theorem, linear matrix inequalities and Lyapunov functional method. These theoretical analysis can characterize fundamental electrical properties of memristive systems and provide convenience for applications in pattern recognition, associative memories, associative learning, etc.. Finally, two numerical examples are given to show the effectiveness of our results. Keywords Memristive neural networks Finite-time synchronization Anti-synchronization