| 人工神经网络预测烷烃密度的研究 |
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| 引用本文： | 王凤英.人工神经网络预测烷烃密度的研究[J].内蒙古师范大学学报(自然科学版),2006,35(1):80-82. |
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| 作者姓名： | 王凤英 |
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| 作者单位： | 青海民族学院化学系,青海西宁810007 |
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| 摘 要： | 对45种烷烃的0阶到4阶分子连接性指数进行主成分分析，将得到的4个主成分变量作为贝叶斯、正则化神经网络的输入特征参数，对烷烃的密度进行预测．预测结果与实验值的相关系数为0．996，预测标准偏差为0．0028．因此，人工神经网络可以作为预测烷烃密度的有效手段．
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| 关 键 词： | 主成分 分子连接性指数 贝叶斯正则化神经网络 密度 |
| 文章编号： | 1001-8735（2006）01-0080-03 |
| 收稿时间： | 2005-07-21 |
| 修稿时间： | 2005年7月21日 |
Application of ANN in predicting densities of alkanes |
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| WANG Feng-ying.Application of ANN in predicting densities of alkanes[J].Journal of Inner Mongolia Normal University(Natural Science Edition),2006,35(1):80-82. |
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| Authors: | WANG Feng-ying |
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| Institution: | Department of Chemistry,Qinghai Nationalities College,Xining 810007,China |
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| Abstract: | The densities of 45 kinds of alkanes are predicted successfully by Bayesian-Regularization neural network in this paper. 4 principal component variables are used as input parameters of the BRNN. Correlation coefficient between the predictions and experiments is 0. 996 and standard deviation is 0. 0028. The ANN is proved to be an effective method of predicting organic properties of compounds. |
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| Keywords: | principal component molecular connective index bayesian-regularization neural network density |
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