Viscosity prediction for six pure refrigerants using different artificial neural networks
Viscosity prediction for six pure refrigerants using different artificial neural networks
复制标题
使用不同的人工神经网络对六种纯制冷剂进行粘度预测
DOI:
10.1016/j.ijrefrig.2018.02.011
复制
发表时间:
2018-04
影响因子:
3.9
通讯作者:
Zhao Gang
中科院分区:
文献类型:
--
作者:
Zhi Liang-Hui;Hu Peng;Chen Long-Xiang;Zhao Gang
The viscosities of six environmentally friendly pure refrigerants with low GWP are predicted based on three artificial neural network (ANN) models: back propagation neural network (BPNN), radial biased function neural network (RBFNN) and adaptive neuro fuzzy interface system (ANFIS). A total of 1089 experimental data are used to train and test the models. Temperature, pressure and density are considered as input variables of networks. The optimal parameters are obtained through the stepwise searching method. The predicted values using the three optimized ANN models with values of experimental data are compared. Moreover, the viscosity of the six refrigerants in saturated liquid state are predicted using all three models in a wide temperature range. The results show that the deviations of almost all data are less than 5.0% and the ANFIS has the best performance.
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影响因子:
3.9
作者:
Ghaderi, Forouzan;Ghaderi, Amir Hosein;Ghaderi, Noushin
通讯作者:
Ghaderi, Noushin
DOI:
10.1016/s0140-7007(96)00073-4
发表时间:
1997-05-01
期刊:
INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID
影响因子:
--
作者:
Klein, SA;McLinden, MO;Laesecke, A
通讯作者:
Laesecke, A
影响因子:
2.2
作者:
Fan, Jing;Zhao, Xiaoming;Guo, Zhikai;Liu, Zhigang
通讯作者:
Liu, Zhigang
影响因子:
2.6
作者:
Fausto Ciotta;J. Trusler;V. Vesovic
通讯作者:
Fausto Ciotta;J. Trusler;V. Vesovic
DOI:
10.6028/jres.117.014
发表时间:
2012
影响因子:
1.5
作者:
Cousins DS;Laesecke A
通讯作者:
Laesecke A