15387251

Source:http://linkedlifedata.com/resource/pubmed/id/15387251

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0205360, umls-concept:C0205410, umls-concept:C0205421, umls-concept:C0242406, umls-concept:C0348080, umls-concept:C0936012, umls-concept:C1711343, umls-concept:C2348867
pubmed:issue	1
pubmed:dateCreated	2004-9-24
pubmed:abstractText	In this paper, we obtain new sufficient conditions ensuring existence, uniqueness, and global asymptotic stability (GAS) of the equilibrium point for a general class of delayed neural networks (DNNs) via nonsmooth analysis, which makes full use of the Lipschitz property of functions defining DNNs. Based on this new tool of nonsmooth analysis, we first obtain a couple of general results concerning the existence and uniqueness of the equilibrium point. Then those results are applied to show that existence assumptions on the equilibrium point in some existing sufficient conditions ensuring GAS are actually unnecessary; and some strong assumptions such as the boundedness of activation functions in some other existing sufficient conditions can be actually dropped. Finally, we derive some new sufficient conditions which are easy to check. Comparison with some related existing results is conducted and advantages are illustrated with examples. Throughout our paper, spectral properties of the matrix (A + Atau) play an important role, which is a distinguished feature from previous studies. Here, A and Atau are, respectively, the feedback and the delayed feedback matrix defining the neural network under consideration.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/101211035
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Jan
pubmed:issn	1045-9227
pubmed:author	pubmed-author:QiHouduoH, pubmed-author:QiLiqunL
pubmed:issnType	Print
pubmed:volume	15
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	99-109
pubmed:dateRevised	2006-11-15
pubmed:meshHeading	pubmed-meshheading:15387251-Neural Networks (Computer)
pubmed:year	2004
pubmed:articleTitle	Deriving sufficient conditions for global asymptotic stability of delayed neural networks via nonsmooth analysis.
pubmed:affiliation	School of Mathematics, The University of New South Wales, Sydney, NSW 2052, Australia.
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't