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Article: Fail-safe stability for dynamic systems using neural-network controllers

TitleFail-safe stability for dynamic systems using neural-network controllers
Authors
Hung, YSLam, S
KeywordsFail-Safe Stability
Neural-Network Control
Issue Date1995
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappai
Citation
Engineering Applications Of Artificial Intelligence, 1995, v. 8 n. 6, p. 625-632 How to Cite?
AbstractThe small gain theorem is used to consider the stability of a neural network controlled system under the condition that some of the neurons may fail with attenuated outputs and possibly with the addition of a constant output bias. Sufficient conditions based on the small gain theorem and the circle criterion are obtained for fail-safe stability. A loop-transformation technique is used to overcome the conservative nature of the small gain approach. © 1996.
Persistent Identifierhttp://hdl.handle.net/10722/155029
ISSN
0952-1976
2021 Impact Factor: 7.802
2020 SCImago Journal Rankings: 1.106

 

DC FieldValueLanguage
dc.contributor.authorHung, YSen_US
dc.contributor.authorLam, Sen_US
dc.date.accessioned2012-08-08T08:31:34Z-
dc.date.available2012-08-08T08:31:34Z-
dc.date.issued1995en_US
dc.identifier.citationEngineering Applications Of Artificial Intelligence, 1995, v. 8 n. 6, p. 625-632en_US
dc.identifier.issn0952-1976en_US
dc.identifier.urihttp://hdl.handle.net/10722/155029-
dc.description.abstractThe small gain theorem is used to consider the stability of a neural network controlled system under the condition that some of the neurons may fail with attenuated outputs and possibly with the addition of a constant output bias. Sufficient conditions based on the small gain theorem and the circle criterion are obtained for fail-safe stability. A loop-transformation technique is used to overcome the conservative nature of the small gain approach. © 1996.en_US
dc.languageengen_US
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappaien_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.subjectFail-Safe Stabilityen_US
dc.subjectNeural-Network Controlen_US
dc.titleFail-safe stability for dynamic systems using neural-network controllersen_US
dc.typeArticleen_US
dc.identifier.emailHung, YS:yshung@eee.hku.hken_US
dc.identifier.authorityHung, YS=rp00220en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0029490171en_US
dc.identifier.volume8en_US
dc.identifier.issue6en_US
dc.identifier.spage625en_US
dc.identifier.epage632en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridHung, YS=8091656200en_US
dc.identifier.scopusauthoridLam, S=7402279420en_US
dc.identifier.issnl0952-1976-

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