中国物理B ›› 2015, Vol. 24 ›› Issue (5): 50201-050201.doi: 10.1088/1674-1056/24/5/050201

• GENERAL •    下一篇

Augmented Lyapunov approach to H state estimation of static neural networks with discrete and distributed time-varying delays

M. Syed Ali, R. Saravanakumar   

  1. Department of Mathematics, Thiruvalluvar University, Vellore-632115, Tamil Nadu, India
  • 收稿日期:2014-09-11 修回日期:2014-12-07 出版日期:2015-05-05 发布日期:2015-05-05
  • 基金资助:
    Project supported by the Fund from National Board of Higher Mathematics (NBHM), New Delhi (Grant No. 2/48/10/2011-R&D-II/865).

Augmented Lyapunov approach to H state estimation of static neural networks with discrete and distributed time-varying delays

M. Syed Ali, R. Saravanakumar   

  1. Department of Mathematics, Thiruvalluvar University, Vellore-632115, Tamil Nadu, India
  • Received:2014-09-11 Revised:2014-12-07 Online:2015-05-05 Published:2015-05-05
  • Contact: M. Syed Ali E-mail:syedgru@gmail.com
  • About author:02.30.Hq; 02.30.Ks; 05.45.-a; 02.10.Yn
  • Supported by:
    Project supported by the Fund from National Board of Higher Mathematics (NBHM), New Delhi (Grant No. 2/48/10/2011-R&D-II/865).

摘要: This paper deals with H state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H state estimation is proposed to estimate the H performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov–Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.

关键词: distributed delay, H state estimation, neural networks, stability analysis

Abstract: This paper deals with H state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H state estimation is proposed to estimate the H performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov–Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.

Key words: distributed delay, H state estimation, neural networks, stability analysis

中图分类号:  (Ordinary differential equations)

  • 02.30.Hq
02.30.Ks (Delay and functional equations) 05.45.-a (Nonlinear dynamics and chaos) 02.10.Yn (Matrix theory)