Yimin Sun, Zhiyu Qiao, and Minghong He, Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition, J. Univ. Sci. Technol. Beijing, 6(1999), No. 1, pp. 24-26.
Cite this article as:
Yimin Sun, Zhiyu Qiao, and Minghong He, Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition, J. Univ. Sci. Technol. Beijing, 6(1999), No. 1, pp. 24-26.
Yimin Sun, Zhiyu Qiao, and Minghong He, Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition, J. Univ. Sci. Technol. Beijing, 6(1999), No. 1, pp. 24-26.
Citation:
Yimin Sun, Zhiyu Qiao, and Minghong He, Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition, J. Univ. Sci. Technol. Beijing, 6(1999), No. 1, pp. 24-26.
Applied Science School, University of Science & Technology Beijing, Beijing 100083, China
National Natural Science Foundation of China, Beijing 100083, China
中文摘要
The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation neural network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were presented to determine the enthalpies of fusion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.
The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation neural network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were presented to determine the enthalpies of fusion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.
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