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

doi:10.1088/1674-1056/24/5/050201

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

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

M. Syed Ali, R. Saravanakumar

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

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/Abstract

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)

M. Syed Ali, R. Saravanakumar. Augmented Lyapunov approach to H_∞ state estimation of static neural networks with discrete and distributed time-varying delays[J]. 中国物理B, 2015, 24(5): 50201-050201.

M. Syed Ali, R. Saravanakumar. Augmented Lyapunov approach to H_∞ state estimation of static neural networks with discrete and distributed time-varying delays[J]. Chin. Phys. B, 2015, 24(5): 50201-050201.

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

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

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