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

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.

Keywords: distributed delay H_∞ state estimation neural networks stability analysis

Received: 11 September 2014
Revised: 07 December 2014
Accepted manuscript online:

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

Fund: Project supported by the Fund from National Board of Higher Mathematics (NBHM), New Delhi (Grant No. 2/48/10/2011-R&D-II/865).

Corresponding Authors: M. Syed Ali
E-mail: syedgru@gmail.com

About author: 02.30.Hq; 02.30.Ks; 05.45.-a; 02.10.Yn

Cite this article:

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

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

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