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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4941

Title: Design of exponential state estimators for neural networks with mixed time delays
Authors: Liu, Y
Wang, Z
Liu, X
Keywords: State estimator
Recurrent neural networks
Discrete and distributed delays
Lyapunov–Krasovskii functional
Linear matrix inequality
Publication Date: 2007
Publisher: Elsevier
Citation: Physics Letters A, 364(5): 401-412, May 2007
Abstract: In this Letter, the state estimation problem is dealt with for a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. We aim at designing a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally exponentially stable in the presence of mixed time delays. By using the Laypunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to 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 simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.
Description: This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.
Sponsorship: This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of Jiangsu Education Committee of China under Grants 05KJB110154 and BK2006064, and the National Natural Science Foundation of China under Grants 10471119 and 10671172.
URI: http://bura.brunel.ac.uk/handle/2438/4941
DOI: http://dx.doi.org/10.1016/j.physleta.2006.12.018
ISSN: 0375-9601
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Computer Science

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