数量结构-活动关系
表面张力
人工神经网络
生物系统
航程(航空)
集合(抽象数据类型)
实验数据
分子描述符
相关系数
财产(哲学)
数据集
算法
数学
计算机科学
热力学
统计
人工智能
材料科学
机器学习
物理
哲学
复合材料
认识论
生物
程序设计语言
作者
Farhad Gharagheizi,Ali Eslamimanesh,Behnam Tirandazi,Amir H. Mohammadi,Dominique Richon
标识
DOI:10.1016/j.ces.2011.06.052
摘要
In this work, the Quantitative Structure–Property Relationship (QSPR) strategy is applied to represent/predict the surface tension of pure chemical compounds at (66.36–977.40) K temperature range. To propose a comprehensive, reliable, and predictive model, 18298 data belonging to experimental surface tension values of 1604 chemical compounds at different temperatures are studied. The Sequential Search mathematical method has been observed to be the only variable search method capable of selection of appropriate model parameters (molecular descriptors) regarding this large data set. To develop the final model, a three-layer Artificial Neural Network has been optimized using the Levenberg–Marquardt (LM) optimization strategy. Using this dedicated strategy, we obtain satisfactory results quantified by the following statistical parameters: absolute average deviations of the represented/predicted properties from existing experimental values: 3.8%, and squared correlation coefficient: 0.985.
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