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NCUEIR > College of Technology > Department of Industrial Education and Technology > Periodical Articles > Item 987654321/13663

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/13663

Title:	A delay-dependent approach to stability for static recurrent neural networks with mixed time-varying delays
Authors:	Lu, Chien-Yu;Su, T. J;Su, Y. H.;Huang, S. C.
Contributors:	工業教育與技術學系
Keywords:	Static recurrent neural networks;Linear matrix inequalities;Global asymptotic stability;Time-varying delay;Leibniz-Newton formula
Date:	2008-07
Issue Date:	2012-08-27T10:41:03Z
Abstract:	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.
Relation:	International J. Innovative Computing, Information and Control, 4(7): 1661
Appears in Collections:	[Department of Industrial Education and Technology] Periodical Articles

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