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An improved stability criterion for generalized neural networks with additive time-varying delays

journal contribution
posted on 2016-01-01, 00:00 authored by R Rakkiyappan, R Sivasamy, J H Park, T H Lee
This paper deals with the problem of stability analysis of generalized neural networks with time delays. It should be noted that additive time-varying delays are taken in the state of the neural networks. A novel augmented Lyapunov–Krasovskii (L–K) functional which involves more information on the activation function of the neural networks and upper bound of the additive time-varying delays is constructed. By introducing some zero equations and using the reciprocal convex combination technique and Finsler׳s lemma, an improved delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be efficiently solved via standard numerical software. Finally, three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.

History

Journal

Neurocomputing

Volume

171

Pagination

615 - 624

Publisher

Elsevier

Location

Netherlands

ISSN

0925-2312

eISSN

1872-8286

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2015 Elsevier