Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Creating artificial neural networks that generalize
Neural Networks
Introduction to artificial neural systems
Introduction to artificial neural systems
Kolmogorov's theorem and multilayer neural networks
Neural Networks
A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
A neural network based model for abnormal pattern recognition of control charts
Computers and Industrial Engineering
A neural network-based procedure for the monitoring of exponential mean
Computers and Industrial Engineering
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
Advanced Engineering Informatics
Computers and Industrial Engineering
Using neural networks to detect the bivariate process variance shifts pattern
Computers and Industrial Engineering
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Computers and Industrial Engineering
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Computers and Industrial Engineering
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A traditional multivariate control chart is shown to be effective in monitoring a multivariate process to signal the out-of-control condition that arises when mean shifts occur. The immediate classification of the signals associated with mean vector shifts can greatly narrow down the set of possible assignable causes, facilitating rapid analysis and corrective action by a technician before numerous nonconforming units have been manufactured. A persistent problem presented by such multivariate control charts, however, concerns the analysis of signals and the provision of any shift-related information. This study develops an artificial neural network-based model to supplement the multivariate χ2 chart. The method not only identifies the characteristic or group of characteristics that cause the signal but also classifies the magnitude of the shifts when the χ2-statistic signals that mean shifts have occurred. The method is described from the perspectives of training and classification. An example of the application of the proposed method is provided. The results demonstrate that the proposed method provides an excellent rate of classification and the output generated by trained network is very strongly correlated with the corresponding actual target value for every quality characteristic. Additionally, general guidelines for the proper implementation of the proposed method are provided.