| 人工神经网络方法与聚乙烯晶体生长速度模型的研究 |
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| 引用本文： | 刘爱虹,禹新良,王学业.人工神经网络方法与聚乙烯晶体生长速度模型的研究[J].计算机与应用化学,2007,24(3):359-362. |
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| 作者姓名： | 刘爱虹 禹新良 王学业 |
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| 作者单位： | 湘潭大学化学学院,湖南,湘潭,411105 |
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| 摘 要： | 用结晶温度,聚乙烯分子量,聚乙烯质量分数等参数,再用人工神经网络ANN(artificial neural network)方法以建立聚乙烯晶体生长速度(lnG)的预报模型。其拟合值、预测值与实验值的相关系数分别为0．9920和0．9844,平均相对误差分别为0．3448和0．3304,结果表明,所建立的模型可以用于聚乙烯晶体生长速度的计算机预报,克服了现有物理模型和数学方程无法应用于多个变量的缺点。因此ANN可为聚合物结晶过程的定量预报提供依据,也为ANN在聚合物其他性质的预报方面提供参考。
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| 关 键 词： | 聚乙烯 晶体生长速度 人工神经网络 |
| 文章编号： | 1001-4160（2007）03-359-362 |
| 修稿时间： | 2006-01-212006-05-11 |
Studies on rates of crystal growth for polyethylene by an artificial neural network method |
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| Liu Aihong,Yu Xinliang,Wang Xueye.Studies on rates of crystal growth for polyethylene by an artificial neural network method[J].Computers and Applied Chemistry,2007,24(3):359-362. |
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| Authors: | Liu Aihong Yu Xinliang Wang Xueye |
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| Affiliation: | College of Chemistry, Xiangtan University, Xiangtan, 411105, Hunan, China |
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| Abstract: | A quantitative structure-property relationship(QSPR)model of crystal growth rates for polyethylene was built by artificial neural network with crystallization temperature(T_c),molecular weight(M_W)and mass fraction(wt.%).The correlation coefficient R of experimental and calculated values for the training set and testing set are 0.9920 and 0.9844 respectively,and the mean errors of re- gression and prediction are 0.3448 and 0.3304 respectively.The calculated results indicate that the model obtained in this paper can be well used to predict the rates of crystal growth for polyethylene.The ANN method not only is one of the useful tools for polymeric crystallization process study,but also can overcome the shortcomings of the existing model that can not fit for multiple variables.This paper encourages the further application of ANN to the other properties of polymers. |
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| Keywords: | polyethylene rates of crystal growth artificial neural network |
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