2009 Second International Symposium on Computational Intelligence and Design Predicting the Dielectric Constants of (Zr0.7Sn0.3)TiO4 Ceramics Using Artificial Neural Network Changsha, Hunan, China December 12-December 14 ISBN: 978-0-7695-3865-5
Back-propagation artificial neural network was developed to predict the dielectric constants of (Zr0.7Sn0.3)TiO4 ceramics. Leave-one out method was used to train the ANN model. Test results showed that the prediction performance of the ANN model is satisfactory: the scatter dots distribute along the 0__45°diagonal line in the scatter diagram, the values of statistical criteria are 0.7489(MSE), 2.01%(MSRE), and 1.3061(VOF) respectively. After being trained, the ANN model was used to predict the dielectric constants of several samples, the prediction errors are 1.06(MSE), 2.78%(MSRE), and 1.6971(VOF) respectively, which show that the prediction performance of the ANN model is satisfactory. The work is helpful of the development of high-performance electronic ceramics and has important theoretical meaning and application value.
Citation:
Wei You, Song Fan, Songlin Wang, Chuanli Yan, Xiangzhou Zhu, Jun Rao, "Predicting the Dielectric Constants of (Zr0.7Sn0.3)TiO4 Ceramics Using Artificial Neural Network," iscid, vol. 2, pp.395-399, 2009 Second International Symposium on Computational Intelligence and Design, 2009 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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