New results on stability criteria for neural networks with time-varying delays

doi:10.1088/1674-1056/20/5/050505

Abstract In this paper, the problem of stability analysis for neural networks with time-varying delays is considered. By constructing a new augmented Lyapunov–Krasovskii's functional and some novel analysis techniques, improved delay-dependent criteria for checking the stability of the neural networks are established. The proposed criteria are presented in terms of linear matrix inequalities (LMIs) which can be easily solved and checked by various convex optimization algorithms. Two numerical examples are included to show the superiority of our results.

Keywords: neural networks time-varying delays stability Lyapunov method

Received: 03 September 2010
Revised: 10 December 2010
Accepted manuscript online:

PACS:

05.45.-a

(Nonlinear dynamics and chaos)

Fund: Project supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute for Information Technology Advancement) (Grant No. IITA-2009-C1090-0904-0007).

Cite this article:

O.M. Kwon, J.W. Kwon, and S.H. Kim New results on stability criteria for neural networks with time-varying delays 2011 Chin. Phys. B 20 050505

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