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Global synchronization for discrete-time stochastic complex networks with randomly occurred nonlinearities and mixed time delays

journal contribution
posted on 06.12.2017, 00:00 authored by Z Wang, Y Wang, Yurong LiuYurong Liu
In this paper, the problem of stochastic synchronization analysis is investigated for a new array of coupled discretetime stochastic complex networks with randomly occurred nonlinearities (RONs) and time delays. The discrete-time complex networks under consideration are subject to: 1) stochastic nonlinearities that occur according to the Bernoulli distributed white noise sequences; 2) stochastic disturbances that enter the coupling term, the delayed coupling term as well as the overall network; and 3) time delays that include both the discrete and distributed ones. Note that the newly introduced RONs and the multiple stochastic disturbances can better reflect the dynamical behaviors of coupled complex networks whose information transmission process is affected by a noisy environment (e.g., internet-based control systems). By constructing a novel Lyapunov-like matrix functional, the idea of delay fractioning is applied to deal with the addressed synchronization analysis problem. By employing a combination of the linear matrix inequality (LMI) techniques, the free-weighting matrix method and stochastic analysis theories, several delay-dependent sufficient conditions are obtained which ensure the asymptotic synchronization in the mean square sense for the discrete-time stochastic complex networks with time delays. The criteria derived are characterized in terms of LMIs whose solution can be solved by utilizing the standard numerical software. A simulation example is presented to show the effectiveness and applicability of the proposed results.

Funding

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

History

Volume

21

Issue

1

Start Page

11

End Page

25

Number of Pages

15

eISSN

1941-0093

ISSN

1045-9227

Location

Piscataway, NJ

Publisher

Institute of Electrical and Electronics Engineers Inc.

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Donghua University; Institute for Resource Industries and Sustainability (IRIS); Yangzhou da xue;

Era Eligible

Yes

Journal

IEEE transactions on neural networks.