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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4716

Title: Bounded H∞ synchronization and state estimation for discrete time-varying stochastic complex for discrete time-varying stochastic complex networks over a finite horizon
Authors: Shen, B
Wang, Z
Liu, X
Keywords: Bounded H-infinity synchronization
Complex networks
Discrete-time networks
Finite horizon
Recursive linear matrix inequalities
Stochastic networks
Time-varying networks
Transient behavior
Publication Date: 2011
Publisher: IEEE
Citation: IEEE Transactions on neural networks, 22(1): 145-157, Jan 2011
Abstract: In this paper, new synchronization and state estimation problems are considered for an array of coupled discrete time-varying stochastic complex networks over a finite horizon. A novel concept of bounded H∞ synchronization is proposed to handle the time-varying nature of the complex networks. Such a concept captures the transient behavior of the time-varying complex network over a finite horizon, where the degree of bounded synchronization is quantified in terms of the H∞-norm. A general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. By utilizing a timevarying real-valued function and the Kronecker product, criteria are established that ensure the bounded H∞ synchronization in terms of a set of recursive linear matrix inequalities (RLMIs), where the RLMIs can be computed recursively by employing available MATLAB toolboxes. The bounded H∞ state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, over a finite horizon, the dynamics of the estimation error is guaranteed to be bounded with a given disturbance attenuation level. Again, an RLMI approach is developed for the state estimation problem. Finally, two simulation examples are exploited to show the effectiveness of the results derived in this paper.
Description: Copyright [2011] 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.
Sponsorship: This work was supported in part by the Engineering and Physical Sciences Research Council of U.K. under Grant GR/S27658/01, the National Natural Science Foundation of China under Grant 61028008 and Grant 60974030, the National 973 Program of China under Grant 2009CB320600, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany.
URI: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05640676
http://bura.brunel.ac.uk/handle/2438/4716
DOI: http://dx.doi.org/10.1109/TNN.2010.2090669
ISSN: 1045–9227
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Computer Science

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