File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Robust approximate pole assignment for second-order systems: Neural network computation

TitleRobust approximate pole assignment for second-order systems: Neural network computation
Authors
Ho, DWCLam, JXu, J
Issue Date1998
Citation
Journal Of Guidance, Control, And Dynamics, 1998, v. 21 n. 6, p. 923-928 How to Cite?
AbstractA recurrent neural network approach to robust approximate pole assignment for second-order systems is proposed. The design is formulated as an unconstrained optimization problem and solved via the gradient-flow approach, which is ideally suited for neural network implementation. Convergence of the gradient flow also is established. Simulation results are used to demonstrate the effectiveness of the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/156488
ISSN
0731-5090
2021 Impact Factor: 2.486
2020 SCImago Journal Rankings: 1.573
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorHo, DWCen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorXu, Jen_US
dc.date.accessioned2012-08-08T08:42:39Z-
dc.date.available2012-08-08T08:42:39Z-
dc.date.issued1998en_US
dc.identifier.citationJournal Of Guidance, Control, And Dynamics, 1998, v. 21 n. 6, p. 923-928en_US
dc.identifier.issn0731-5090en_US
dc.identifier.urihttp://hdl.handle.net/10722/156488-
dc.description.abstractA recurrent neural network approach to robust approximate pole assignment for second-order systems is proposed. The design is formulated as an unconstrained optimization problem and solved via the gradient-flow approach, which is ideally suited for neural network implementation. Convergence of the gradient flow also is established. Simulation results are used to demonstrate the effectiveness of the proposed method.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Guidance, Control, and Dynamicsen_US
dc.titleRobust approximate pole assignment for second-order systems: Neural network computationen_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.scopuseid_2-s2.0-0032207947en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0032207947&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume21en_US
dc.identifier.issue6en_US
dc.identifier.spage923en_US
dc.identifier.epage928en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridHo, DWC=7402971938en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.scopusauthoridXu, J=35276508700en_US
dc.identifier.issnl0731-5090-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats