Prediction of high weight polymers glass transition temperature using RBF neural networks

dc.contributor.author	Αφαντίτης, Ανδρέας	el
dc.contributor.author	Μελαγράκη, Γεωργία	el
dc.contributor.author	Μακρυδήμα, Καλλιόπη	el
dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.date.accessioned	2015-06-04T13:21:14Z
dc.date.available	2015-06-04T13:21:14Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15059
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.elsevier.com/	en
dc.subject	Glass transition temperature
dc.subject	QSPR
dc.subject	RBF neural network
dc.subject	Θερμοκρασία υαλώδους μετάπτωσης
dc.title	Prediction of high weight polymers glass transition temperature using RBF neural networks	en
heal.type	journalArticle
heal.classification	Science
heal.classification	Physics
heal.classification	Επιστήμη
heal.classification	Φυσική
heal.classificationURI	http://zbw.eu/stw/descriptor/15685-2
heal.classificationURI	http://zbw.eu/stw/descriptor/15669-0
heal.classificationURI	N/A-Επιστήμη
heal.classificationURI	N/A-Φυσική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh94004665
heal.contributorName	Ιγγλέση-Μαρκοπούλου, Όλγα	el
heal.identifier.secondary	DOI: 10.1016/j.theochem.2004.11.021
heal.language	en
heal.access	campus
heal.publicationDate	2007-03-07
heal.bibliographicCitation	AFANTITIS, A., MELAGRAKI, G., MAKRIDIMA, A., ALEXANDRIDIS, A.P., SARIMVEIS, H.K., et al. (2005). Prediction of high weight polymers glass transition temperature using RBF neural networks. Journal of Molecular Structure: THEOCHEM. [online] 716 (1-3). p. 193-198. Available from: http://www.elsevier.com/[Accessed 05/01/2005]	en
heal.abstract	A novel approach to the prediction of the glass transition temperature (Tg) for high molecular polymers is presented. A new quantitative structure-property relationship (QSPR) model is obtained using Radial Basis Function (RBF) neural networks and a set of four-parameter descriptors, ∑MV(ter)(Rter), LF, ΔXSB and ∑PEI. The produced QSPR model (R2=0.9269) proved to be considerably more accurate compared to a multiple linear regression model (R2=0.8227).	en
heal.publisher	Elsevier	en
heal.journalName	Journal of Molecular Structure: THEOCHEM	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true