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

被引：80

作者：

Afantitis, A ^{[1
]}

Melagraki, G ^{[1
]}

Makridima, K ^{[1
]}

Alexandridis, A ^{[1
]}

Sarimveis, H ^{[1
]}

Iglessi-Markopoulou, O ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Sch Chem Engn, Athens 15780, Greece

来源：

JOURNAL OF MOLECULAR STRUCTURE-THEOCHEM | 2005年 / 716卷 / 1-3期

关键词：

RBF neural network; QSPR; glass transition temperature;

D O I：

10.1016/j.theochem.2004.11.021

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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.

引用

页码：193 / 198

页数：6

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