| 人工神经网络预测含硫芳香族化合物的水解速率常数 |
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| 引用本文： | 王霞 孙莹. 人工神经网络预测含硫芳香族化合物的水解速率常数[J]. 南开大学学报(自然科学版), 1997, 30(1): 102-104,109 |
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| 作者姓名： | 王霞 孙莹 |
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| 作者单位： | 南开大学化学系!天津，300071 |
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| 摘 要： | 本文利用误差反向传播（ＢＰ）的人工神经网络（ＡＮＮ）模型处理低度非线性问题。研究了含硫芳香族化合物水解速率常数与其结构及物理化学参数之间的定量结构活性相关（ＱＳＡＲ）。预测结果与实验结果符合良好，优于多元回归方法（ＭＬＲ）所得的结果。
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| 关 键 词： | 神经网络 芳香族 含硫化物 水解速率常数 |
FORECAST THE HYDROLYSIS SPEED CONSTANT OF AROMATIC SULFUR-CONTAINING COMPOUNDS BY ANN |
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| Wang Xia,Sun Ying, Yuan Manxue, Lai Chengming. FORECAST THE HYDROLYSIS SPEED CONSTANT OF AROMATIC SULFUR-CONTAINING COMPOUNDS BY ANN[J]. Acta Scientiarum Naturalium University Nankaiensis, 1997, 30(1): 102-104,109 |
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| Authors: | Wang Xia Sun Ying Yuan Manxue Lai Chengming |
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| Abstract: | We dealt with low-nonlinear problem by Artificial Neural Networks(ANNs)using standard back-propagation(BP).Then to study the Quantitative Structure Activity Rela-tionship (QSAR) between hydrolysis speed conbstant of arorpatic sulfur-contaning com-pounds and their structure,physicaland chemical parameters,The results obtained in this paper were superior to those of multiregression analysis and in the agreement with the ex-perimental datas. |
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| Keywords: | Artificial Neural Network (ANN) Quantitative Sructure Activity Relationship (QSAR) aromatic sulfur-contaning compounds |
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