Table of Contents Author Guidelines Submit a Manuscript
Journal of Nanomaterials
Volume 2015 (2015), Article ID 950943, 11 pages
Research Article

An Ensemble Learning for Predicting Breakdown Field Strength of Polyimide Nanocomposite Films

1School of Applied Science, Harbin University of Science and Technology, Harbin 150080, China
2College of Computer Science and Engineering, Dalian Nationalities University, 18 Liaohe West Road, Dalian Development Zone, Dalian 116600, China
3Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, China
4Faculty of Engineering, Mudanjiang Normal College, Mudanjiang 157012, China

Received 8 April 2015; Accepted 31 May 2015

Academic Editor: Mircea Chipara

Copyright © 2015 Hai Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Using the method of Stochastic Gradient Boosting, ten SMO-SVR are constructed into a strong prediction model (SGBS model) that is efficient in predicting the breakdown field strength. Adopting the method of in situ polymerization, thirty-two samples of nanocomposite films with different percentage compositions, components, and thicknesses are prepared. Then, the breakdown field strength is tested by using voltage test equipment. From the test results, the correlation coefficient (CC), the mean absolute error (MAE), the root mean squared error (RMSE), the relative absolute error (RAE), and the root relative squared error (RRSE) are 0.9664, 14.2598, 19.684, 22.26%, and 25.01% with SGBS model. The result indicates that the predicted values fit well with the measured ones. Comparisons between models such as linear regression, BP, GRNN, SVR, and SMO-SVR have also been made under the same conditions. They show that CC of the SGBS model is higher than those of other models. Nevertheless, the MAE, RMSE, RAE, and RRSE of the SGBS model are lower than those of other models. This demonstrates that the SGBS model is better than other models in predicting the breakdown field strength of polyimide nanocomposite films.