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首页> 外文会议>Conference on Ecological Informatics and Ecosystem Conservation >Comparison of Genetic Algorithm Based Support Vector Machine and Genetic Algorithm Based RBF Neural Network in Quantitative Structure-Property Relationship Models on Aqueous Solubility of Polycyclic Aromatic Hydrocarbons
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Comparison of Genetic Algorithm Based Support Vector Machine and Genetic Algorithm Based RBF Neural Network in Quantitative Structure-Property Relationship Models on Aqueous Solubility of Polycyclic Aromatic Hydrocarbons

机译：基于遗传算法基于遗传算法的基于支持向量机和基于RBF神经网络的定量结构性质关系模型对多环芳烃水溶性的基于RBF神经网络

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摘要

A modified method to develop quantitative structure-property relationship (QSPR) models of organic contaminants was proposed based on genetic algorithm (GA) and support vector machine (SVM). GA was used to perform the variable selection and SVM was used to construct QSPR model. In this study, GA-SVM was applied to develop the QSPR model for aqueous solubility (S_w, mg·l~(-1)) of polycyclic aromatic hydrocarbons (PAHs). The R~2 (0.980), SSE (2.84), and RMSE (0.25) values of the model developed by GA-SVM indicated a good predictive capability for logSw values of PAHs. Based on leave-one-out cross validation, the results of GA-SVM were compared with those of genetic algorithm-radial based function neural network (GA-RBENN). The comparison showed that the R~2 (0.923) and RMSE (0.485) values of GA-SVM were higher and lower, respectively, which illustrated GA-SVM was more suitable to develop QSPR model for the logSw values of PAHs than GA-RBFNN.
机译：基于遗传算法（GA）和支持向量机（SVM），提出了一种制定有机污染物的定量结构性质关系（QSPR）模型的修改方法。 GA用于执行变量选择，使用SVM来构建QSPR模型。在该研究中，应用GA-SVM为多环芳烃（PAH）的水溶性（S_W，Mg·L〜（-1））开发QSPR模型。 GA-SVM开发的模型的R〜2（0.980），SSE（2.84）和RMSE（0.25）值表明了PAHS的LOGSW值的良好预测能力。基于休假交叉验证，与基于遗传算法的函数神经网络（GA-RBenn）进行比较了Ga-SVM的结果。比较表明，GA-SVM的R〜2（0.923）和RMSE（0.485）分别较高，下面的GA-SVM更适合于开发PAHS的LOGSW值的QSPR模型而不是GA-RBFNN 。

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