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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4920

Title: State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays
Authors: Liu, Y
Wang, Z
Liu, X
Keywords: Discrete-time neural networks
Mixed time delays
Markovian jumping parameters
State estimator
Linear matrix inequality
Publication Date: 2008
Publisher: Elsevier
Citation: Physics Letters A, 372(48): 7147-7155, Dec 2008
Abstract: In this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov–Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions.
Description: This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier Ltd
Sponsorship: This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01 and EP/C524586/1, an International Joint Project sponsored by the Royal Society of the UK, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, the National Natural Science Foundation of China under Grant 60774073, and the Alexander von Humboldt Foundation of Germany.
URI: http://bura.brunel.ac.uk/handle/2438/4920
DOI: http://dx.doi.org/10.1016/j.physleta.2008.10.045
ISSN: 0375-9601
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers

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