Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11122
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dc.contributor.authorLikas, A.en
dc.contributor.authorKarras, D. A.en
dc.contributor.authorLagaris, I. E.en
dc.date.accessioned2015-11-24T17:03:01Z-
dc.date.available2015-11-24T17:03:01Z-
dc.identifier.issn0020-7160-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11122-
dc.rightsDefault Licence-
dc.subjectneural network simulationen
dc.subjecttemplateen
dc.subjectmerlinen
dc.subjecttraining strategyen
dc.subjectreinforcementen
dc.titleNeural network training and simulation using a multidimensional optimization systemen
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.publicationDate1998-
heal.abstractA new approach is presented to neural network simulation and training that is based on the use of general purpose optimization software. This approach requires that the training problem should be formulated as the minimization of a cost function of the network weights. This cost function is a user written code called by the optimization system, which in turn provides the user with a variety of minimization procedures that can be combined via user programmable minimization strategies. Experimental results concerning several learning paradigms indicate that the approach is very convenient and effective and leads to the discovery of efficient training strategies.en
heal.journalNameInternational Journal of Computer Mathematicsen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)



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