A new approach to stability analysis of neural networks with time-varying delay via novel Lyapunov--Krasovskii functional

doi:10.1088/1674-1056/19/5/050507

Abstract In this paper, new delay-dependent stability criteria for asymptotic stability of neural networks with time-varying delays are derived. The stability conditions are represented in terms of linear matrix inequalities (LMIs) by constructing new Lyapunov--Krasovskii functional. The proposed functional has an augmented quadratic form with states as well as the nonlinear function to consider the sector and the slope constraints. The less conservativeness of the proposed stability criteria can be guaranteed by using convex properties of the nonlinear function which satisfies the sector and slope bound. Numerical examples are presented to show the effectiveness of the proposed method.

Keywords: neural networks Lyapunov--Krasovskii functional sector bound time-delay

Received: 19 August 2009
Revised: 29 October 2009
Accepted manuscript online:

PACS:	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	02.10.Yn	(Matrix theory)
	02.30.Yy	(Control theory)
	02.60.Dc	(Numerical linear algebra)

Cite this article:

S. M. Lee, O. M. Kwon, and Ju H. Park A new approach to stability analysis of neural networks with time-varying delay via novel Lyapunov--Krasovskii functional 2010 Chin. Phys. B 19 050507

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A new approach to stability analysis of neural networks with time-varying delay via novel Lyapunov--Krasovskii functional

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