National Changhua University of Education Institutional Repository : Item 987654321/13672

English | 正體中文 | 简体中文 | Items with full text/Total items : 6507/11669
Visitors : 30124768 Online Users : 468

RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope Adv. Search

Login ‧ Upload ‧ Help ‧ About ‧ Administer

NCUEIR > College of Technology > Department of Industrial Education and Technology > Periodical Articles > Item 987654321/13672

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/13672

Title:	Delay-dependent approach to robust stability for uncertain discrete stochastic recurrent neural networks with interval time-varying delays
Authors:	Lu, Chien-Yu;Shyr, W. J.;Yao, K. C.;Chen, D. F.
Contributors:	工業教育與技術學系
Keywords:	Discrete stochastic recurrent neural networks;Interval time-varying delays;Linear matrix inequality;Robuststability;Uncertainty
Date:	2009-09
Issue Date:	2012-08-27T10:42:01Z
Publisher:	ICIC Express Letters Office
Abstract:	This paper considers the problem of global robust delay-dependent stabilityfor uncertain discrete stochastic recurrent neural networks with intervaltime-varying delays. The parameter uncertainties are assumed to be time-varyingnorm-bounded in the state equation. The activation functions are assumed to beglobally Lipschitz continuous. Based on an appropriate Lyapunov-Krasovskiifunctional, global robust delay-dependent stability criterion which is dependenton both the lower bound and upper bound of the interval time-varying delays isderived by introducing some slack matrix variables. A sufficient condition forthe discrete stochastic recurrent neural networks with interval time-varyingdelays is presented in terms of the linear matrix inequality (LMI). A Numericalexample is given to demonstrate the reduced conservatism of the proposed resultsin this paper.
Relation:	ICIC Express Letters, 3(3): 457-463
Appears in Collections:	[Department of Industrial Education and Technology] Periodical Articles

Files in This Item:

File	Size	Format
2030100910012.pdf	85Kb	Adobe PDF	551	View/Open