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Section A

Improved asymptotical stability criteria for static recurrent neural networks

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Pages 597-605 | Received 05 Jun 2011, Accepted 19 Dec 2011, Published online: 13 Feb 2012
 

Abstract

In this paper, the problem of asymptotical stability for static recurrent neural networks is investigated. Based on delay partitioning approach and a new Lyapunov–Krasvoskii functional, delay-independent conditions are proposed to ensure the asymptotic stability of the static recurrent neural networks. The delay-independent conditions are less conservative than the existing ones. Expressed in linear matrix inequalities, the stability conditions can be checked using the standard numerical software. Two numerical examples are provided to illustrate the effectiveness and the reduced conservatism of the proposed results.

2000 AMS Subject Classifications :

Acknowledgements

This work is partly supported by the Nature Science Foundation of Chongqing (CSTC, 2009BB2378, 2008BB2199).

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