Random forest and multilayer perceptron for predicting the dielectric loss of polyimide nanocomposite films. Issue 49 (15th June 2017)

• Record Type: Journal Article
• Authors: Guo, H.; Zhao, J. Y.; Yin, J. H.
• Is Part Of: RSC advances. Volume 7:Issue 49(2017)
• Page Start: 30999
• Page End: 31008
• Publication Date: 2017-06-15
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c7ra04147k
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : A random forest and multilayer perceptron for predicting the dielectric loss of polyimide nanocomposite films. As shown in the experimental results, the error between the predicted value and the measured value is small. Abstract : As a new insulating material, nanoscale thin dielectric films have been widely used in variable frequency motors, electron devices and other fields. Dielectric loss is a key performance parameter of this material. Currently, studies on the dielectric properties of polymer matrix nanocomposite films are based mostly on experiments that are costly and time-consuming. In this article, an integrated method that combines experiment and ensemble learning is applied. In situ polymerization is employed to prepare 32 polyimide matrix nanocomposite films that have different weight ratios, sizes and thicknesses and that are doped with different inorganic nanoscale particles. The dielectric losses of these 32 prepared films are measured as well. Ten multilayer perceptrons are integrated into a random forest and multilayer perceptron (RF-MLP) model using the random forest (RF) method. As shown in the experimental results, under the 10-fold cross validation, the correlation coefficient, the mean absolute error, the root mean squared error and the root relative squared error of the RF-MLP model are 0.9447, 0.0007, 0.0013 and 32.0972%, respectively. The deviation between the predicted value and the measured value is small. The RF-MLP model has a better prediction performance than other single models, such as linear regression, backpropagation neural network, radial basis function neural network, support vector regression and multilayer perceptron as well as other ensemble learning methods, such as bagging, boosting and RF-decision stump. Therefore, the RF-MLP model is a fast and reliable method applicable to predicting the properties of the new nano-dielectric material and other materials.