[1]刘伟,刘赞,王玲玲..神经网络与结构编码法预测直馏汽油色谱保留指数[J].郑州大学学报(工学版),2004,25(03):26-28.[doi:10.3969/j.issn.1671-6833.2004.03.007]
　LIU Wei,LIU Zan,Wang Lingling.Neural network and structural coding method to predict the chromatographic retention index of straight-run gasoline[J].Journal of Zhengzhou University (Engineering Science),2004,25(03):26-28.[doi:10.3969/j.issn.1671-6833.2004.03.007]
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神经网络与结构编码法预测直馏汽油色谱保留指数()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
25卷
期数:
2004年03期
页码:
26-28
栏目:
出版日期:
1900-01-01

文章信息/Info

Title:
Neural network and structural coding method to predict the chromatographic retention index of straight-run gasoline
作者:
刘伟; 刘赞; 王玲玲.
郑州大学生物工程系,河南,郑州,450052, 河南省环境监测中心站,河南,郑州,450004
Author(s):
LIU Wei; LIU Zan; Wang Lingling
关键词:
神经网络;  结构编码;  气相色谱保留指数
Keywords:
DOI:
10.3969/j.issn.1671-6833.2004.03.007
文献标志码:
A
摘要:
对直馏汽油中的单体烃的分子结构进行了数字编码,并采用误差反向传播神经网络算法构造了直馏汽油中单体烃的气相色谱保留指数与其分子结构的非线性相关模型,神经网络结构为3层,隐含层节点为7个,有15个输入,对应单体烃的15位数字编码,1个输出,对应气相色谱保留指数.预测结果表明,由误差反传算法所得的相关系数和标准偏差均优于多元线性回归方法.
Abstract:
The molecular structure of monomeric hydrocarbons in straight-run gasoline is digitally encoded, and the nonlinear correlation model between the gas chromatographic retention index and its molecular structure of monomeric hydrocarbons in straight-run gasoline is constructed by error backpropagation neural network algorithm, the neural network structure is 3 layers, the hidden layer nodes are 7, there are 15 inputs, corresponding to the 15-bit digital code of the monomer hydrocarbons, and 1 output, corresponding to the gas chromatographic retention index. The prediction results show that the correlation coefficient and standard deviation obtained by the error back-transmission algorithm are better than those obtained by the multiple linear regression method.

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更新日期/Last Update: 1900-01-01