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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/13671

Title: A delay-dependent approach to design state estimation for discrete stochastic recurrent neural network with interval time-varying delays
Authors: Liao, W. C.;Lu, Chien-Yu;Zheng, K. Y.;Ting, C. C.
Contributors: 工業教育與技術學系
Keywords: Recurrent neural network;Stochastic systems;Linear matrix inequality;State estimators;Interval time-delays
Date: 2009-09
Issue Date: 2012-08-27T10:41:58Z
Publisher: ICIC
Abstract: This paper deals with the problem of state estimation for discrete stochastic recurrent neural network with interval time-delays. The activation functions are assumed to be globally Lipschitz continuous. Attention is focused on the design of a state estimator which ensures the global stability of the estimation error dynamics. A delay-dependent condition with dependence on the upper and lower bounds of the delays is given in terms
of a linear matrix inequality (LMI) to solve the neuron state estimation problem. When this LMI is feasible, the expression of a desired state estimator is also presented. In addition, slack matrices are introduced to reduce the conservatism of the condition. A numerical example is provided to demonstrate the applicability of the proposed approach.
Relation: ICIC Express Letters, 3(3A): 465-470
Appears in Collections:[工業教育與技術學系] 期刊論文

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