中国物理B ›› 2003, Vol. 12 ›› Issue (1): 22-24.doi: 10.1088/1009-1963/12/1/304

• GENERAL • 上一篇    下一篇

Global exponential convergence analysis of delayed cellular neural networks

张 强1, 马润年1, 王 超1, 许 进2   

  1. (1)Institute of Electronic Engineering, Xidian University, Xi'an 710071, China; (2)Institute of System Science, Huazhong University of Science and Technology, Wuhan 430074, China
  • 收稿日期:2002-02-26 修回日期:2002-07-06 出版日期:2003-01-20 发布日期:2003-01-20
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No 69971018).

Global exponential convergence analysis of delayed cellular neural networks

Zhang Qiang (张 强)a, Ma Run-Nian (马润年)a, Wang Chao (王 超)a, Xu Jin (许 进)b   

  1. a Institute of Electronic Engineering, Xidian University, Xi'an 710071, China; b Institute of System Science, Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:2002-02-26 Revised:2002-07-06 Online:2003-01-20 Published:2003-01-20
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No 69971018).

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摘要： Some sufficient criteria have been established to ensure the global exponential stability of delayed cellular neural networks by using an approach based on delay differential inequality. Compared with the method of Lyapunov functionals as in most previous studies, our method is simpler and more effective for a stability analysis of delayed system. Some previously established results in the literature are shown to be special cases of the present result.

Abstract: Some sufficient criteria have been established to ensure the global exponential stability of delayed cellular neural networks by using an approach based on delay differential inequality. Compared with the method of Lyapunov functionals as in most previous studies, our method is simpler and more effective for a stability analysis of delayed system. Some previously established results in the literature are shown to be special cases of the present result.

Key words: global exponential stability, delayed cellular neural networks, delay differentialinequality, Lyapunov functionals

中图分类号:  (Neural networks)

  • 84.35.+i
02.60.-x (Numerical approximation and analysis)