The Strength of Weak Learnability
Machine Learning
Neural networks and the bias/variance dilemma
Neural Computation
Network generalization differences quantified
Neural Networks
Machine Learning
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
Computers and Industrial Engineering
An ensemble of neural networks for weather forecasting
Neural Computing and Applications
Artificial neural networks to classify mean shifts from multivariate χ2 chart signals
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Using neural networks to detect the bivariate process variance shifts pattern
Computers and Industrial Engineering
Computers and Industrial Engineering
Distributed decision support system for airport ground handling management using WSN and MAS
Engineering Applications of Artificial Intelligence
Computers and Industrial Engineering
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In multivariate statistical process control (MSPC), most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon an overall statistic. But these charts do not relieve the need for pinpointing source(s) of the out-of-control signals. Neural networks (NNs) have excellent noise tolerance and high pattern identification capability in real time, which have been applied successfully in MSPC. This study proposed a selective NN ensemble approach DPSOEN, where several selected NNs are jointly used to classify source(s) of out-of-control signals in multivariate processes. The immediate location of the abnormal source(s) can greatly narrow down the set of possible assignable causes, facilitating rapid analysis and corrective action by quality operators. The performance of DPSOEN is analyzed in multivariate processes. It shows improved generalization performance that outperforms those of single NNs and Ensemble All approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to identify abnormal source(s) in bivariate statistical process control (SPC) with potential application for MSPC in general.