数学季刊 ›› 2007, Vol. 22 ›› Issue (2): 195-202.

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非对称细胞神经网络的稳定性分析

  


  1. 1. Detriment of Mathematics, Huzhou Teacher's College  2. College of Mathematics Physics and Information Science, Zhejiang Normal University 

  • 收稿日期:2004-03-23 出版日期:2007-06-30 发布日期:2023-11-01
  • 作者简介: LI Li-ping(1974-), male, native of Linchuan, Jiangxi, a lecturer of Huzhou Toacher's College, M.S.D., engages in ordinary diferential equation and dynamic system.
  • 基金资助:
     Supported by the NSF of Zhejiang Province(M103087); Supported by the Science Research Fund of Hushou Teacher’s College;

Stability Analysis of Nonsymmetric Cellular Neural Networks


  1. 1. Detriment of Mathematics, Huzhou Teacher's College  2. College of Mathematics Physics and Information Science, Zhejiang Normal University 

  • Received:2004-03-23 Online:2007-06-30 Published:2023-11-01
  • About author: LI Li-ping(1974-), male, native of Linchuan, Jiangxi, a lecturer of Huzhou Toacher's College, M.S.D., engages in ordinary diferential equation and dynamic system.
  • Supported by:
     Supported by the NSF of Zhejiang Province(M103087); Supported by the Science Research Fund of Hushou Teacher’s College;

摘要: This paper describes the problem of stability for one-dimensional Cellular Neural Networks(CNNs). A sufficient condition is presented to ensure complete stability for a class of special CNN’s with nonsymmetric templates, where the parameter in the output function is greater than or equal to zero. The main method is analysising the property of the equilibrium point of the CNNs system.


关键词: cellular neural networks, stability, equilibrium point, limit set

Abstract: This paper describes the problem of stability for one-dimensional Cellular Neural Networks(CNNs). A sufficient condition is presented to ensure complete stability for a class of special CNN’s with nonsymmetric templates, where the parameter in the output function is greater than or equal to zero. The main method is analysising the property of the equilibrium point of the CNNs system.


Key words: cellular neural networks, stability, equilibrium point, limit set

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