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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11110
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dc.contributor.authorLikas, A.en
dc.contributor.authorPapageorgiou, G.en
dc.contributor.authorStafylopatis, A.en
dc.date.accessioned2015-11-24T17:02:55Z-
dc.date.available2015-11-24T17:02:55Z-
dc.identifier.issn0924-669X-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11110-
dc.rightsDefault Licence-
dc.subjectconstraint satisfactionen
dc.subjectneural network architecturesen
dc.subjecthopfield networken
dc.subjectboltzmann machineen
dc.subjectfrequency assignment problemen
dc.subjectneural networksen
dc.subjectchannel assignmenten
dc.subjectoptimization problemsen
dc.subjectarchitectureen
dc.subjectalgorithmen
dc.titleA connectionist approach for solving large constraint satisfaction problemsen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate1997-
heal.abstractAn efficient neural network technique is presented for the solution of binary constraint satisfaction problems. The method is based on the application of a double-update technique to the operation of the discrete Hopfield-type neural network that can be constructed for the solution of such problems. This operation scheme ensures that the network moves only between consistent states, such that each problem variable is assigned exactly one value, and leads to a fast and efficient search of the problem state space. Extensions of the proposed method are considered in order to include several optimisation criteria in the search. Experimental results concerning many real-size instances of the Radio Links Frequency Assignment Problem demonstrate very good performance.en
heal.journalNameApplied Intelligenceen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
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