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Robust stability of discrete-time stochastic neural networks with time-varying delays

journal contribution
posted on 06.12.2017, 00:00 by Yurong LiuYurong Liu, Z Wang, X Liu
In this paper, the global exponential stability problem is studied for a class of discrete-time uncertain stochastic neural networks with time delays. The stability analysis problem is investigated, for the first time, for such kind of neural networks. In the neural network model, the parameter uncertainties are norm-bounded, the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion, and the delay is time-varying. By utilizing a Lyapunov-Krasovskii functional and using some well-known inequalities, we convert the addressed stability analysis problem into the feasibility problem of several linear matrix inequalities (LMIs). Different from the commonly used matrix norm theories (such as the M-matrix method), a unified LMI approach is developed to establish sufficient conditions for the neural networks to be globally, robustly, exponentially stable. A numerical example is provided to show the usefulness of the proposed global stability condition.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

71

Issue

4-6

Start Page

823

End Page

833

Number of Pages

11

ISSN

0925-2312

Location

Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Journal

Neurocomputing.