File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A new approach to exponential stability analysis of neural networks with time-varying delays

TitleA new approach to exponential stability analysis of neural networks with time-varying delays
Authors
Issue Date2006
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/neunet
Citation
Neural Networks, 2006, v. 19 n. 1, p. 76-83 How to Cite?
AbstractThis paper considers the problem of exponential stability analysis of neural networks with time-varying delays. The activation functions are assumed to be globally Lipschitz continuous. A linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the delayed neural network to have a unique equilibrium point, which is globally exponentially stable. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the reduced conservativeness of the proposed results. © 2005 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/156806
ISSN
2015 Impact Factor: 3.216
2015 SCImago Journal Rankings: 1.629
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorXu, Sen_US
dc.contributor.authorLam, Jen_US
dc.date.accessioned2012-08-08T08:44:03Z-
dc.date.available2012-08-08T08:44:03Z-
dc.date.issued2006en_US
dc.identifier.citationNeural Networks, 2006, v. 19 n. 1, p. 76-83en_US
dc.identifier.issn0893-6080en_US
dc.identifier.urihttp://hdl.handle.net/10722/156806-
dc.description.abstractThis paper considers the problem of exponential stability analysis of neural networks with time-varying delays. The activation functions are assumed to be globally Lipschitz continuous. A linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the delayed neural network to have a unique equilibrium point, which is globally exponentially stable. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the reduced conservativeness of the proposed results. © 2005 Elsevier Ltd. All rights reserved.en_US
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/neuneten_US
dc.relation.ispartofNeural Networksen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshLinear Modelsen_US
dc.subject.meshNeural Networks (Computer)en_US
dc.subject.meshTime Factorsen_US
dc.titleA new approach to exponential stability analysis of neural networks with time-varying delaysen_US
dc.typeArticleen_US
dc.identifier.emailLam, J:james.lam@hku.hken_US
dc.identifier.authorityLam, J=rp00133en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.neunet.2005.05.005en_US
dc.identifier.pmid16153804-
dc.identifier.scopuseid_2-s2.0-30644469165en_US
dc.identifier.hkuros120205-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-30644469165&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume19en_US
dc.identifier.issue1en_US
dc.identifier.spage76en_US
dc.identifier.epage83en_US
dc.identifier.isiWOS:000235261500004-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridXu, S=7404438591en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats