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题名: A delay-dependent approach to design state estimation for discrete stochastic recurrent neural network with interval time-varying delays
作者: Liao, W. C.;Lu, Chien-Yu;Zheng, K. Y.;Ting, C. C.
贡献者: 工業教育與技術學系
关键词: Recurrent neural network;Stochastic systems;Linear matrix inequality;State estimators;Interval time-delays
日期: 2009-09
上传时间: 2012-08-27T10:41:58Z
出版者: ICIC
摘要: 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.
關聯: ICIC Express Letters, 3(3A): 465-470
显示于类别:[工業教育與技術學系] 期刊論文


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