Synthetic neural networks for process control
Computers and Industrial Engineering
The Strength of Weak Learnability
Machine Learning
Neural networks and the bias/variance dilemma
Neural Computation
Control charts in the presence of data correlation
Management Science
A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
Machine Learning
A statistical control chart for stationary process data
Technometrics
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
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Computers and Industrial Engineering
Measuring the performance improvement of a double generally weighted moving average control chart
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, a neural network-based identification model is proposed for both mean and variance shifts in correlated processes. The proposed model uses a selective network ensemble approach named DPSOEN to obtain the improved generalization performance, which outperforms those of single neural network. The model is capable of on-line monitoring mean and variance shifts, and classifying the types of shifts without considering the occurrence of both mean and variance shifts in one time. This model is unique since all learning-based methods developed so far can only detect mean or variance shift, but are incapable of classifying types of shifts. The result is significant since it provides additional useful information about the process changes, which can greatly aid identification of assignable causes. The simulation results demonstrate that the model outperforms the conventional control charts in terms of average run length (ARL), and can classify the types of shifts in a real-mode.