National Changhua University of Education Institutional Repository : Item 987654321/13663
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题名: A delay-dependent approach to stability for static recurrent neural networks with mixed time-varying delays
作者: Lu, Chien-Yu;Su, T. J;Su, Y. H.;Huang, S. C.
贡献者: 工業教育與技術學系
关键词: Static recurrent neural networks;Linear matrix inequalities;Global asymptotic stability;Time-varying delay;Leibniz-Newton formula
日期: 2008-07
上传时间: 2012-08-27T10:41:03Z
摘要: This paper performs a global stability analysis of a particular class of recurrent neural networks (RNN) in the static neural network models with both discrete and distributed time-varying delays. Both Lipschitz continuous activation function and monotone nondecreasing activation function are considered. Globally delay-dependent stability criteria are derived in the form of linear matrix inequalities (LMI) through the use of Leibniz-Newton formula and relaxation matrices. Moreover, the constraint that derivative of time-varying delays must be smaller than one is released for the proposed control scheme. Finally, two numerical examples are given to illustrate the effectiveness of the proposed criterion.
關聯: International J. Innovative Computing, Information and Control, 4(7): 1661
显示于类别:[工業教育與技術學系] 期刊論文

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