Title :
A SNCCDBAGG-Based NN Ensemble Approach for Quality Prediction in Injection Molding Process
Author :
Liu, Yang ; Wang, Fu-li ; Chang, Yu-Qing ; Li, Chuang
Author_Institution :
Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
fDate :
4/1/2011 12:00:00 AM
Abstract :
This paper presents a SNCCDBAGG-based neural network (NN) ensemble approach for quality prediction in injection molding process. Bagging is used to create NNs for the ensemble by independently training these NNs on different training sets. Negative correlation learning via correlation-corrected data (NCCD) is used to achieve negative correlation of each network´s error against errors for the rest of the ensemble by training transformed target data for NN in the ensemble as the desired network output for some epochs. A selection-based strategy is proposed to improve generalization ability when combining Bagging and NCCD. Experimental results show its good performance on quality predicting in injection molding process compared with single NN predictor and NCCD predictor.
Keywords :
injection moulding; neural nets; production engineering computing; quality control; SNCCDBAGG-based NN ensemble approach; bagging; injection molding process; negative correlation learning via correlation-corrected data; neural network; quality prediction; Artificial neural networks; Bagging; Injection molding; Prediction algorithms; Testing; Training; Training data; Bagging; generalization ability; injection molding process; neural network (NN) ensemble;
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
DOI :
10.1109/TASE.2010.2077279