Correlating of Thermal Conductivity of monatomic Gases Using Artificial Neural Networks

被引：0

作者：

Melzi, Naima ^{[1
]}

Khaouane, Latifa ^{[1
]}

Hanini, Salah ^{[1
]}

Laidi, Maamar ^{[1
]}

机构：

[1] Univ Medea, Lab Biomat & Transport Phenomena LBMPT, Medea, Algeria

来源：

PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON APPLIED SMART SYSTEMS (ICASS) | 2018年

关键词：

Artificial neural network; Modeling; Prediction; Thermal conductivity; monatomic gases;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Aset of 119 monatomic Gas was used to train validation and test the performance of the ANN, good correlations were found (R=0.993 for NN). The root mean squared errors for the total data set were 1%, and mean square errors (MSE) 0.01 for NN. Moreover, it was revealed by the comparison between the forecasted outcomes and other models that the neural network models provided greater results.

引用

页数：3

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