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Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide

Authors: R Tabaraki, T Khayamian, Ali A Ensafi
Publication date: 2006/9/30
Journal name: Journal of Molecular Graphics and Modelling
Volume: 25
Pages: 46-54
Publisher: Elsevier
 
A wavelet neural network (WNN) model in quantitative structure property relationship
(QSPR) was developed for predicting solubility of 25 anthraquinone dyes in supercritical
carbon dioxide over a wide range of pressures (70–770bar) and temperatures (291–423K).
A large number of descriptors were calculated with Dragon software and a subset of
calculated descriptors was selected from 18 classes of Dragon descriptors with a stepwise
multiple linear regression (MLR) as a feature selection technique. Six calculated and two ...

http://www.sciencedirect.com/science/article/pii/S109332630500149X

Journal Papers
Month/Season: 
September
Year: 
2006

تحت نظارت وف ایرانی

Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide | Prof. Ali A. Ensafi

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تحت نظارت وف ایرانی