【摘 要】
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Utilized degradable data of coal-filled films from the accelerated UV chamber ageing degradation experiments, and on the basis of control factors\' analysis, presented a predicting model on degradable properties of this film in photo-degradation accordi
【机 构】
：
Dept.of Material Science and Technology, Xi’an University of Science & Technology, Xi’an 710054, Chi
论文部分内容阅读
Utilized degradable data of coal-filled films from the accelerated UV chamber ageing degradation experiments, and on the basis of control factors\' analysis, presented a predicting model on degradable properties of this film in photo-degradation according to back-propagation artificial neural network (BP ANN). 4 controlling factors in films degradation, including temperature, the time of UV irradiation, the concentration and the type of coals were used as input parameters in the ANN model. While the degradable properties after film degradation, including the mechanical properties and carbonyl index, were used as output parameters. It was carried out by the neural network toolbox of Matlab 6.5 software and Visual Basic 6.0. Discussed partition of sample data and model\'s parameters,and then selected the best configuration of ANN network. The accurate scope of predicting results was analyzed. This model has a high precision in predicting on properties of the coal-filled film degradation.
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