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Robust state estimation for discrete-time stochastic neural networks with probabilistic measurement delays

journal contribution
posted on 06.12.2017, 00:00 by Z Wang, Yurong LiuYurong Liu, X Liu, Y Shi
In this paper, the robust H-infinity state estimation problem is investigated for a general class of uncertain discrete-time stochastic neural networks with probabilistic measurement delays. The measurement delays of the neural networks are described by a binary switching sequence satisfying a conditional probability distribution. The neural network under study involves parameter uncertainties, stochastic disturbances and time-varying delays, and the activation functions are characterized by sector-like nonlinearities. The problem addressed is the design of a full-order state estimator, for all admissible uncertainties, nonlinearities and time-delays, the dynamics of the estimation error is constrained to be robustly exponentially stable in the mean square and, at the same time, a prescribed H1 disturbance rejection attenuation level is guaranteed. By using the Lyapunov stability theory and stochastic analysis techniques, sufficient conditions are first established to ensure the existence of the desired estimators. These conditions are dependent on the lower and upper bounds of the time-varying delays. Then, the explicit expression of the desired estimator gains is described in terms of the solution to a linear matrix inequality (LMI). Finally, a numerical example is exploited to show the usefulness of the results derived.

Funding

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

History

Volume

74

Issue

1-3

Start Page

256

End Page

264

Number of Pages

9

eISSN

1872-8286

ISSN

0925-2312

Location

Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Brunel University; Donghua University; TBA Research Institute; Yangzhou da xue; Zhongguo ke xue yuan;

Era Eligible

Yes

Journal

Neurocomputing.