Global stability of Clifford-valued recurrent neural networks with time delays

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• 作者：Yang Liu ; Pei Xu ; Jianquan Lu ; Jinling Liang
• 关键词：Clifford ; valued recurrent neural networks ; Time delay ; Global asymptotic stability ; Global exponential stability
• 刊名：Nonlinear Dynamics
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：84
• 期：2
• 页码：767-777
• 全文大小：539 KB
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• 作者单位：Yang Liu (1) (2)
  Pei Xu (1)
  Jianquan Lu (2)
  Jinling Liang (2)
  
  1. Department of Mathematics, Zhejiang Normal University, Jinhua, 321004, China
  2. Department of Mathematics, Southeast University, Nanjing, 210096, China
  
• 刊物类别：Engineering
• 刊物主题：Vibration, Dynamical Systems and Control
  Mechanics
  Mechanical Engineering
  Automotive and Aerospace Engineering and Traffic
  
• 出版者：Springer Netherlands
• ISSN：1573-269X

文摘

In this paper, we study an issue of stability analysis for Clifford-valued recurrent neural networks (RNNs) with time delays. As an extension of real-valued neural network, the Clifford-valued neural network, which includes familiar complex-valued neural network and quaternion-valued neural network as special cases, has been an active research field recently. To the best of our knowledge, the stability problem for Clifford-valued systems with time delays has still not been solved. We first explore the existence and uniqueness for the equilibrium of delayed Clifford-valued RNNs, based on which some sufficient conditions ensuring the global asymptotic and exponential stability of such systems are obtained in terms of a linear matrix inequality (LMI). The simulation result of a numerical example is also provided to substantiate the effectiveness of the proposed results.