ESTIMATION OF THE ACID STRENGTH OF MIXED OXIDES BY A NEURAL NETWORK

被引：28

作者：

KITO, S ^{[1
]}

HATTORI, T ^{[1
]}

MURAKAMI, Y ^{[1
]}

机构：

[1] NAGOYA UNIV,DEPT APPL CHEM,CHIKUSA KU,NAGOYA 46401,JAPAN

来源：

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH | 1992年 / 31卷 / 03期

关键词：

D O I：

10.1021/ie00003a046

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

An artificial neural network is applied to the estimation of strength of acid sites synergistically generated in binary mixed oxides. The acid strength is represented as a function of physical/chemical properties of both constituent oxides. The form of the function is represented by a back-propagation neural network which was trained by using the published acid strength data for such cases. The estimated results were in a good agreement with the experimentally observed ones.

引用

页码：979 / 981

页数：3

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