A note on multivariate CUSUM procedures
Technometrics
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Back-propagation pattern recognizers for X¯ control charts: methodology and performance
Computers and Industrial Engineering
A probabilistic resource allocating network for novelty detection
Neural Computation
A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
Unnatural pattern recognition on control charts using correlation analysis techniques
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
A comparison of multivariate normal generators
Communications of the ACM
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Random Projection RBF Nets for Multidimensional Density Estimation
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
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
Review: A review of novelty detection
Signal Processing
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Non-random (abnormal) behaviour indicates that a process is under the influence of special causes of variation. Detection of abnormal patterns is well established in univariate statistical process control (SPC). Various solutions including heuristics, traditional computer programming, expert systems and neural networks (NNs) have been successfully implemented. In multivariate SPC (MSPC), on the other hand, there is a clear need for more investigations into pattern detection. Bivariate SPC is a special case of MSPC where the number of variates is two and is studied here in terms of identification of shift patterns. In this work, an existing NN classification technique-known as novelty detection (ND)--whose application for MSPC has not been reported is applied for pattern recognition. ND successfully detects non-random bivariate time-series patterns representing shifts of various magnitudes in the process mean vector. The investigation proposes a simple heuristic approach for applying ND as an effective and useful tool for pattern detection in bivariate SPC with potential applicability for MSPC in general.