中国物理B ›› 2009, Vol. 18 ›› Issue (12): 5203-5211.doi: 10.1088/1674-1056/18/12/017

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A delay-decomposition approach for stability of neural network with time-varying delay

崔宝同1, 籍艳1, 邱芳2   

  1. (1)College of Communications and Control Engineering, Jiangnan University, Wuxi 214122, China; (2)College of Communications and Control Engineering, Jiangnan University, Wuxi 214122, China;Department of Mathematics, Binzhou University, Binzhou 256603, China
  • 收稿日期:2009-04-01 修回日期:2009-05-19 出版日期:2009-12-20 发布日期:2009-12-20
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No 60674026) and the Natural Science Foundation of Jiangsu Province of China (Grant No BK2007016).

A delay-decomposition approach for stability of neural network with time-varying delay

Qiu Fang(邱芳)a)b)†, Cui Bao-Tong (崔宝同)a), and Ji Yan(籍艳)a)   

  1. a College of Communications and Control Engineering, Jiangnan University, Wuxi 214122, China; b Department of Mathematics, Binzhou University, Binzhou 256603, China
  • Received:2009-04-01 Revised:2009-05-19 Online:2009-12-20 Published:2009-12-20
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No 60674026) and the Natural Science Foundation of Jiangsu Province of China (Grant No BK2007016).

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摘要： This paper studies delay-dependent asymptotical stability problems for the neural system with time-varying delay. By dividing the whole interval into multiple segments such that each segment has a different Lyapunov matrix, some improved delay-dependent stability conditions are derived by employing an integral equality technique. A numerical example is given to demonstrate the effectiveness and less conservativeness of the proposed methods.

Abstract: This paper studies delay-dependent asymptotical stability problems for the neural system with time-varying delay. By dividing the whole interval into multiple segments such that each segment has a different Lyapunov matrix, some improved delay-dependent stability conditions are derived by employing an integral equality technique. A numerical example is given to demonstrate the effectiveness and less conservativeness of the proposed methods.

Key words: neural system, global asymptotical stability, time-varying delay

中图分类号:  (Neural networks, fuzzy logic, artificial intelligence)

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02.10.Yn (Matrix theory)