中国物理B ›› 2011, Vol. 20 ›› Issue (4): 40204-040204.doi: 10.1088/1674-1056/20/4/040204

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Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay

S. Lakshmanan, P. Balasubramaniam   

  1. Department of Mathematics, Gandhigram Rural University, Gandhigram { 624 302, Tamilnadu, India
  • 收稿日期:2010-10-29 修回日期:2010-11-29 出版日期:2011-04-15 发布日期:2011-04-15
  • 基金资助:
    Project supported by the Science Foundation of the Department of Science and Technology, New Delhi, India (Grant No. SR/S4/MS:485/07).

Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay

S. Lakshmanan and P. Balasubramaniam   

  1. Department of Mathematics, Gandhigram Rural University, Gandhigram -624 302, Tamilnadu, India
  • Received:2010-10-29 Revised:2010-11-29 Online:2011-04-15 Published:2011-04-15
  • Supported by:
    Project supported by the Science Foundation of the Department of Science and Technology, New Delhi, India (Grant No. SR/S4/MS:485/07).

摘要: This paper studies the problem of linear matrix inequality (LMI) approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.

Abstract: This paper studies the problem of linear matrix inequality (LMI) approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.

Key words: delay-dependent stability, linear matrix inequality, Lyapunov--Krasovskii functional, stochastic neural networks

中图分类号:  (Stochastic processes)

  • 02.50.Ey
02.50.Fz (Stochastic analysis) 07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)