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

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dc.contributor.author Afantitis, A en
dc.contributor.author Melagraki, G en
dc.contributor.author Makridima, K en
dc.contributor.author Alexandridis, A en
dc.contributor.author Sarimveis, H en
dc.contributor.author Iglessi-Markopoulou, O en
dc.date.accessioned 2014-03-01T01:22:58Z
dc.date.available 2014-03-01T01:22:58Z
dc.date.issued 2005 en
dc.identifier.issn 0166-1280 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/16746
dc.subject Glass transition temperature en
dc.subject QSPR en
dc.subject RBF neural network en
dc.subject.classification Chemistry, Physical en
dc.subject.other poly(1 pentene) en
dc.subject.other poly(1,1 dichloroethylene) en
dc.subject.other poly(2 chlorostyrene) en
dc.subject.other poly(3 methylstyrene) en
dc.subject.other poly(4 chlorostyrene) en
dc.subject.other poly(4 fluorostyrene) en
dc.subject.other poly(a methylstyrene) en
dc.subject.other poly(butylacrylate) en
dc.subject.other poly(butylethylene) en
dc.subject.other poly(chlorotrifluoroethylene) en
dc.subject.other poly(cyclohexylethylene) en
dc.subject.other poly(ethylchloroacrylate) en
dc.subject.other poly(ethylmethylacrylate) en
dc.subject.other poly(methyl methacrylate) en
dc.subject.other poly(n heptylacrylate) en
dc.subject.other poly(n hexylacrylate) en
dc.subject.other poly(n octylacrylate) en
dc.subject.other poly(oxyethylene) en
dc.subject.other poly(oxyoctamethylene) en
dc.subject.other poly(oxytetramethylene) en
dc.subject.other poly(tert butylacrylate) en
dc.subject.other poly(tert butylmethylacrylate) en
dc.subject.other poly(vinyl n butyl ether) en
dc.subject.other poly(vinyl n octyl ether) en
dc.subject.other poly(vinylhexyl ether) en
dc.subject.other polyethylene en
dc.subject.other polymer en
dc.subject.other polyvinyl acetate en
dc.subject.other polyvinylchloride en
dc.subject.other unclassified drug en
dc.subject.other unindexed drug en
dc.subject.other accuracy en
dc.subject.other article en
dc.subject.other artificial neural network en
dc.subject.other comparative study en
dc.subject.other glass transition temperature en
dc.subject.other linear regression analysis en
dc.subject.other mathematical model en
dc.subject.other molecular weight en
dc.subject.other multiple regression en
dc.subject.other parameter en
dc.subject.other prediction en
dc.subject.other quantitative structure activity relation en
dc.subject.other theoretical model en
dc.title Prediction of high weight polymers glass transition temperature using RBF neural networks en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.theochem.2004.11.021 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.theochem.2004.11.021 en
heal.language English en
heal.publicationDate 2005 en
heal.abstract A novel approach to the prediction of the glass transition temperature (T,) 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, Sigma MV(ter)(R-ter), L-F, AX(SB) and Sigma PEI. The produced QSPR model (R-2 = 0.9269) proved to be considerably more accurate compared to a multiple linear regression model (R-2 =0.8227). (c) 2004 Elsevier B.V. All rights reserved. en
heal.publisher ELSEVIER SCIENCE BV en
heal.journalName Journal of Molecular Structure: THEOCHEM en
dc.identifier.doi 10.1016/j.theochem.2004.11.021 en
dc.identifier.isi ISI:000227966200024 en
dc.identifier.volume 716 en
dc.identifier.issue 1-3 en
dc.identifier.spage 193 en
dc.identifier.epage 198 en


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