A co-training style semi-supervised artificial neural network modeling and its application in thermal conductivity prediction of polymeric composites filled with BN sheets  

作　　者：Yunmin Liang Zhichun Liu Wei Liu 

机构地区：[1]School of Energy and Power Engineering,Huazhong University of Science and Technology,1037 Luoyu Road,Hongshan District,Wuhan 430074,China

出　　处：《Energy and AI》2021年第2期172-180,共9页能源与人工智能（英文）

基　　金：The research was financially supported by the National Natural Sci-ence Foundation of China(Nos.51776079 and 51736004).

摘　　要：Predicting the thermal conductivity of polymeric composites filled with BN sheets is helpful for fabricating ther-mal management material.In this study,a co-training style semi-supervised artificial neural network model(Co-ANN)was proposed to take advantage of unlabeled data to refine the prediction.The thermal conductivity of polymer matrix,the diameter,aspect ratio,and volume fraction of the BN sheets are considered as the input variables of the thermal conduction model.Two artificial neural network(ANN)learners with different archi-tecture will label the unlabeled examples.Through estimating the labeling confidence from the mathematical influence and thermal conductive behavior,the most confidently labeled example will be used to augment the training dataset.The lower limit of the labeling confidence is introduced to reduce the data noise.After learn-ing the augmented training information,a combination of two ANN regressors will construct the final Co-ANN thermal conduction model.Compared to other models,the newly developed Co-ANN thermal conduction model remarkably improves the thermal conductivity prediction and exhibits the best accuracy and generalization per-formance.The proposed method shows a vast potential in thermal conductive material design.

关 键 词：Polymeric composites BN sheets Semi-supervised regression Thermal conductivity Artificial neural network 

分 类 号：TB3[一般工业技术—材料科学与工程]