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DC Field | Value | Language |
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dc.contributor.author | Liang, J | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Liu, X | - |
dc.coverage.spatial | 13 | en |
dc.date.accessioned | 2010-01-13T16:29:26Z | - |
dc.date.available | 2010-01-13T16:29:26Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks. 20(5): 781-793 | en |
dc.identifier.issn | 1045-9227 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/4033 | - |
dc.description | Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. | - |
dc.description.abstract | This paper is concerned with the problem of state estimation for a class of discrete-time coupled uncertain stochastic complex networks with missing measurements and time-varying delay. The parameter uncertainties are assumed to be norm-bounded and enter into both the network state and the network output. The stochastic Brownian motions affect not only the coupling term of the network but also the overall network dynamics. The nonlinear terms that satisfy the usual Lipschitz conditions exist in both the state and measurement equations. Through available output measurements described by a binary switching sequence that obeys a conditional probability distribution, we aim to design a state estimator to estimate the network states such that, for all admissible parameter uncertainties and time-varying delays, the dynamics of the estimation error is guaranteed to be globally exponentially stable in the mean square. By employing the Lyapunov functional method combined with the stochastic analysis approach, several delay-dependent criteria are established that ensure the existence of the desired estimator gains, and then the explicit expression of such estimator gains is characterized in terms of the solution to certain linear matrix inequalities (LMIs). Two numerical examples are exploited to illustrate the effectiveness of the proposed estimator design schemes. | en |
dc.language.iso | en | en |
dc.publisher | IEEE | en |
dc.subject | Complex networks | en |
dc.subject | Discrete-time systems | en |
dc.subject | Lyapunov functional | en |
dc.subject | Missing measurements | en |
dc.subject | Parameter uncertainties | en |
dc.subject | State estimator | en |
dc.subject | Stochastic disturbances | en |
dc.title | State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: The discrete-time case | en |
dc.type | Research Paper | en |
dc.identifier.doi | http://dx.doi.org/10.1109/TNN.2009.2013240 | - |
Appears in Collections: | Computer Science Dept of Computer Science Research Papers |
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