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Existence and stability of periodic solution of high-order discrete-time Cohen-Grossberg neural networks with varying delays

机译:高阶离散时变Cohen-Grossberg神经网络周期解的存在性和稳定性

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摘要

This paper studies the existence and stability of periodic solution of the high-order discrete-time Cohen-Grossberg neural networks with varying delays. The properties of M-matrix and the contracting mapping principle are used to obtain a sufficient condition that guarantees the uniqueness and global exponential stability of the periodic solution. In addition, a numerical example is given that demonstrates the effectiveness of the proposed theoretical results.
机译:本文研究了具有变化时滞的高阶离散时间Cohen-Grossberg神经网络周期解的存在性和稳定性。利用M-矩阵的性质和收缩映射原理来获得保证周期解唯一性和全局指数稳定性的充分条件。另外,给出了一个数值例子,证明了所提出的理论结果的有效性。

著录项

  • 来源
    《Neurocomputing》 |2015年第ptac期|1445-1450|共6页
  • 作者单位

    School of Science, Linyi University, Linyi, Shandong 276005, China,School of Mathematical Sciences, Shandong Normal University, Jinan, Shandong 250014, China;

    School of Automobile Engineering, Linyi University, Linyi, Shandong 276005, China;

    School of Science, Linyi University, Linyi, Shandong 276005, China,Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI 02881, USA;

    School of Automobile Engineering, Linyi University, Linyi, Shandong 276005, China;

    School of Informatics, Linyi University, Linyi, Shandong 276005, China;

    School of Informatics, Linyi University, Linyi, Shandong 276005, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cohen-Grossberg neural network; Discrete-time; High-order; Periodic solution; Global stability;

    机译:Cohen-Grossberg神经网络;离散时间;高阶定期解决方案;全球稳定;

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