Exponential stability of delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions

doi:10.1016/j.neunet.2012.01.006

Residential College	false
Status	已發表Published
	Exponential stability of delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions
	Song X.; Xin X.; Huang W.
	2012
Source Publication	Neural Networks
ISSN	8936080
Volume	29-30 Pages:80
Abstract	The paper discusses exponential stability of distributed delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions. By relative nonlinear measure method, some novel criteria are obtained for the uniqueness and exponential stability of the equilibrium point. Our method abandons usual assumptions on global Lipschitz continuity, boundedness and monotonicity of activation functions. Our results are generalization and improvement of some existing ones. Finally, two examples and their simulations are presented to illustrate the correctness of our analysis. © 2012 Elsevier Ltd.
Keyword	Cellular Neural Networks Distributed Delays Exponential Stability Partial Lipschitz Continuity Relative Nonlinear Measure
DOI	10.1016/j.neunet.2012.01.006
URL	View the original
Language	英語English
WOS ID	WOS:000304239600010
The Source to Article	Scopus
Scopus ID	2-s2.0-84862801064
Fulltext Access	View Full-Text via DOI View Full-Text via Web of Science View Full-Text via Scopus
Citation statistics
Document Type	Journal article
Collection	University of Macau
Recommended Citation GB/T 7714	Song X.,Xin X.,Huang W.. Exponential stability of delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions[J]. Neural Networks, 2012, 29-30, 80.
APA	Song X.., Xin X.., & Huang W. (2012). Exponential stability of delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions. Neural Networks, 29-30, 80.
MLA	Song X.,et al."Exponential stability of delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions".Neural Networks 29-30(2012):80.

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