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Article: Neural computation for robust approximate pole assignment

TitleNeural computation for robust approximate pole assignment
Authors
KeywordsApproximate Pole Assignment
Gradient Flow
Neural Networks
Output Feedback
Robustness
Issue Date1999
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/neucom
Citation
Neurocomputing, 1999, v. 25 n. 1-3, p. 191-211 How to Cite?
AbstractThis paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method. | This paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/156513
ISSN
2015 Impact Factor: 2.392
2015 SCImago Journal Rankings: 1.202
References

 

DC FieldValueLanguage
dc.contributor.authorHo, DWCen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorXu, Jen_US
dc.contributor.authorKa Tam, Hen_US
dc.date.accessioned2012-08-08T08:42:45Z-
dc.date.available2012-08-08T08:42:45Z-
dc.date.issued1999en_US
dc.identifier.citationNeurocomputing, 1999, v. 25 n. 1-3, p. 191-211en_US
dc.identifier.issn0925-2312en_US
dc.identifier.urihttp://hdl.handle.net/10722/156513-
dc.description.abstractThis paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method. | This paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/neucomen_US
dc.relation.ispartofNeurocomputingen_US
dc.subjectApproximate Pole Assignmenten_US
dc.subjectGradient Flowen_US
dc.subjectNeural Networksen_US
dc.subjectOutput Feedbacken_US
dc.subjectRobustnessen_US
dc.titleNeural computation for robust approximate pole assignmenten_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/S0925-2312(99)00057-0en_US
dc.identifier.scopuseid_2-s2.0-0032962144en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0032962144&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume25en_US
dc.identifier.issue1-3en_US
dc.identifier.spage191en_US
dc.identifier.epage211en_US
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridHo, DWC=7402971938en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.scopusauthoridXu, J=35276508700en_US
dc.identifier.scopusauthoridKa Tam, H=6504467953en_US

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