| 人工神经网络预测阴离子表面活性剂的临界胶束浓度 |
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| 引用本文： | 于涛,岳金明,丁伟,刘春天.人工神经网络预测阴离子表面活性剂的临界胶束浓度[J].日用化学工业,2006,36(5):277-279. |
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| 作者姓名： | 于涛 岳金明 丁伟 刘春天 |
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| 作者单位： | 1. 大庆石油学院,化学化工学院,黑龙江,大庆,163318
2. 大庆油田博士后科研工作站,黑龙江,大庆,163411;中国科学院,理化技术研究所,博士后流动站,北京,100101 |
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| 摘 要： | 根据定量结构-性能关系原理,利用人工神经网络模型,采用Wn、S和Sn三种拓扑指数描述阴离子表面活性剂的分子结构并作为网络输入,预测阴离子表面活性剂的临界胶束浓度。确定了网络参数,利用43组数据对网络进行训练和预测,并与文献值进行了比较。结果表明,预测精度较高,说明人工神经网络方法具有很好的预测能力。
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| 关 键 词： | 阴离子表面活性剂 临界胶束浓度 人工神经网络 定量结构-性能关系 |
| 文章编号： | 1001-1803(2006)05-0277-03 |
| 收稿时间： | 04 10 2006 12:00AM |
| 修稿时间： | 05 15 2006 12:00AM |
Prediction of critical micelle concentration of anionic surfactant by artificial neural network |
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| YU Tao,YUE Jin-ming,DING Wei,LIU Chun-tian.Prediction of critical micelle concentration of anionic surfactant by artificial neural network[J].China Surfactant Detergent & Cosmetics,2006,36(5):277-279. |
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| Authors: | YU Tao YUE Jin-ming DING Wei LIU Chun-tian |
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| Affiliation: | 1. Chemistry and Chemical Engineering Institute, Daqing Petroleum Institute, Daqing 163318, China; 2. Daqing Oilfield Mobile Postdoctoral Center, Daqing 163411, China; 3. Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | Based on quantitative structure-property relationship(QSPR),utilizing artificial network,three topological(parameters) W_n,S and S_n were used to describe molecular structure of anionic surfactants and as inputs to the network to(predict) critical micelle concentration of anionic surfactant.The parameters of network were identified.Altogether,there were 43 groups of data applied for training and prediction.Results were compared with that proclaimed in literatures and showed that the prediction was very accurate and the network approach is effective in this(application). |
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| Keywords: | anionic surfactant critical micelle concentration artificial neural network quantitative structure-property(relationship)(QSPR) |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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