کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9591831 | 1507015 | 2005 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Support vector regression based QSPR for the prediction of some physicochemical properties of alkyl benzenes
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی تئوریک و عملی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Physicochemical properties of alkyl benzenes are essential to separate pure component from alkyl benzene mixture. Support vector regression (SVR), a novel powerful machine learning technology based on statistical learning theory (SLT), integrated with topological indices was applied to the prediction of five physicochemical properties of alkyl benzenes including the normal boiling point (bp), enthalpy of vaporization at the boiling point (Hvb), critical temperature (Tc), critical pressure (Pc), and critical volume (Vc). In a benchmark test, SVR models for bp, Hvb, Tc, Pc, and Vc were compared with several modeling techniques currently used in this field. The prediction accuracy of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the prediction accuracy of SVR model was higher than those of back propagation artificial neural network (BP ANN) and partial least squares (PLS) methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Molecular Structure: THEOCHEM - Volume 719, Issues 1â3, 14 April 2005, Pages 119-127
Journal: Journal of Molecular Structure: THEOCHEM - Volume 719, Issues 1â3, 14 April 2005, Pages 119-127
نویسندگان
Shansheng Yang, Wencong Lu, Nianyi Chen, Qiannan Hu,