Global exponential stability of reaction–diffusion neural networks with discrete and distributed time-varying delays

doi:10.1088/1674-1056/20/3/030701

Abstract This paper investigates the global exponential stability of reaction–diffusion neural networks with discrete and distributed time-varying delays. By constructing a more general type of Lyapunov–Krasovskii functional combined with a free-weighting matrix approach and analysis techniques, delay-dependent exponential stability criteria are derived in the form of linear matrix inequalities. The obtained results are dependent on the size of the time-varying delays and the measure of the space, which are usually less conservative than delay-independent and space-independent ones. These results are easy to check, and improve upon the existing stability results. Some remarks are given to show the advantages of the obtained results over the previous results. A numerical example has been presented to show the usefulness of the derived linear matrix inequality (LMI)-based stability conditions.

Keywords: neural networks reaction–diffusion delays exponential stability

Received: 07 July 2010
Revised: 28 September 2010
Accepted manuscript online:

PACS:

07.05.Mh

(Neural networks, fuzzy logic, artificial intelligence)

Fund: Project partially supported by the National Natural Science Foundation of China (Grant No. 60974139) and partially supported by the Fundamental Research Funds for the Central Universities.

Cite this article:

Zhang Wei-Yuan(张为元) and Li Jun-Min(李俊民) Global exponential stability of reaction–diffusion neural networks with discrete and distributed time-varying delays 2011 Chin. Phys. B 20 030701

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Global exponential stability of reaction–diffusion neural networks with discrete and distributed time-varying delays

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