Application of neural networks to unit commitment

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dc.contributor.authorYalcinoz T.
dc.contributor.authorShort M.J.
dc.contributor.authorCory B.J.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued1999
dc.departmentNiğde ÖHÜ
dc.descriptionProceedings of the 1999 5th IEEE AFRICON Conference 'Electrotechnical Services for Africa' -- 28 September 1999 through 1 October 1999 -- Cape Town, S Afr -- 56393
dc.description.abstractIn this paper, an improved Hopfield neural network which was described in [1] is applied to unit commitment problem. A new mapping process has been used and a computational method for obtaining the weights and biases is described using a slack variable technique for handling inequality constraints.
dc.identifier.endpage654
dc.identifier.scopus2-s2.0-0033332153
dc.identifier.scopusqualityN/A
dc.identifier.startpage649
dc.identifier.urihttps://hdl.handle.net/11480/1405
dc.identifier.volume2
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherIEEE, Piscataway, NJ, United States
dc.relation.ispartofIEEE AFRICON Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleApplication of neural networks to unit commitment
dc.typeConference Object

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