Name
Title | ||
|---|---|---|
Predicting the dielectric constants of (Zr0.7Sn 0.3)TiO4 ceramics using artificial neural network |
Abstract | ||
|---|---|---|
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. © 2009 IEEE. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISCID.2009.245 | ISCID 2009 - 2009 International Symposium on Computational Intelligence and Design |
Keywords | Field | DocType |
null | Ceramic,Permittivity,Pattern recognition,Zirconium compounds,Computer science,Dielectric,Artificial intelligence,Backpropagation,Artificial neural network,Scatter plot,Back propagation artificial neural network,Machine learning | Conference |
Volume | Issue | ISSN |
2 | null | null |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei You | 1 | 11 | 4.20 |
| Song Fan | 2 | 0 | 0.34 |
| Songlin Wang | 3 | 0 | 0.34 |
| Chuanli Yan | 4 | 0 | 0.34 |
| Xiangzhou Zhu | 5 | 0 | 0.34 |
| Jun Rao | 6 | 0 | 0.34 |