Viscosity prediction for six pure refrigerants using different artificial neural networks

Viscosity prediction for six pure refrigerants using different artificial neural networks
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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
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发表时间:
2018-04
影响因子:
3.9
通讯作者:
Zhao Gang
Zhao Gang
中科院分区:
工程技术2区
文献类型:
--
作者:
Zhi Liang-Hui;Hu Peng;Chen Long-Xiang;Zhao Gang

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基于3种人工神经网络模型:反向传播神经网络(BPNN)、径向偏倚函数神经网络(RBFNN)和自适应神经模糊界面系统(ANFIS),对6种低GWP环保型纯制冷剂的粘度进行了预测。总共使用了1089个实验数据来训练和测试模型。将温度、压力和密度作为网络的输入变量。通过逐步搜索法得到最优参数。将优化后的三种人工神经网络模型的预测值与实验数据值进行了比较。此外,在较宽的温度范围内,利用这三种模型对六种制冷剂的饱和液态粘度进行了预测。结果表明,几乎所有数据的偏差都小于5.0%,ANFIS具有最好的性能。
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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发表时间: 2013-09-01
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