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Conference Paper: Stability analysis for dynamical neural network systems

TitleStability analysis for dynamical neural network systems
Authors
Lam, SHung, YS
KeywordsComputers
Artificial intelligence
Issue Date1994
PublisherIEEE.
Citation
Ieee International Conference On Neural Networks - Conference Proceedings, 1994, v. 2, p. 960-963 How to Cite?
DOI: http://dx.doi.org/10.1109/ICNN.1994.374311
AbstractIn this paper, the small gain theorem will be used to establish a criterion for the stability of a feedback system containing a feedforward neural network. A method for the determination of the gain of a piecewise-linear feedforward neural network is introduced and applied to the stability analysis for a control system consisting of a LTI SISO system with a dynamic ANN controller.
Persistent Identifierhttp://hdl.handle.net/10722/46214
ISSN
1098-7576

 

DC FieldValueLanguage
dc.contributor.authorLam, Sen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2007-10-30T06:44:57Z-
dc.date.available2007-10-30T06:44:57Z-
dc.date.issued1994en_HK
dc.identifier.citationIeee International Conference On Neural Networks - Conference Proceedings, 1994, v. 2, p. 960-963en_HK
dc.identifier.issn1098-7576en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46214-
dc.description.abstractIn this paper, the small gain theorem will be used to establish a criterion for the stability of a feedback system containing a feedforward neural network. A method for the determination of the gain of a piecewise-linear feedforward neural network is introduced and applied to the stability analysis for a control system consisting of a LTI SISO system with a dynamic ANN controller.en_HK
dc.format.extent299001 bytes-
dc.format.extent3891 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE International Conference on Neural Networks - Conference Proceedingsen_HK
dc.rights©1994 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectComputersen_HK
dc.subjectArtificial intelligenceen_HK
dc.titleStability analysis for dynamical neural network systemsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1098-7576&volume=2&spage=960&epage=963&date=1994&atitle=Stability+analysis+for+dynamical+neural+network+systemsen_HK
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICNN.1994.374311en_HK
dc.identifier.scopuseid_2-s2.0-0028743336en_HK
dc.identifier.hkuros5501-
dc.identifier.volume2en_HK
dc.identifier.spage960en_HK
dc.identifier.epage963en_HK
dc.identifier.scopusauthoridLam, S=7402279658en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.issnl1098-7576-

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