Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Using autocorrelations, cusums and runs rules for control chart pattern recognition: an expert system approach
Design of a knowledge-based expert system for statistical process control
Computers and Industrial Engineering
A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems
Computers and Industrial Engineering
The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Expert Systems with Applications: An International Journal
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
Lessons in neural network training: overfitting may be harder than expected
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
The effect of attribute scaling on the performance of support vector machines
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
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The effective recognition of unnatural control chart patterns (CCPs) is a critical issue in statistical process control, as unnatural CCPs can be associated with specific assignable causes adversely affecting the process. Machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. However, ANN approaches can easily overfit the training data, producing models that can suffer from the difficulty of generalization. This causes a pattern misclassification problem when the training examples contain a high level of background noise (common cause variation). Support vector machines (SVMs) embody the structural risk minimization, which has been shown to be superior to the traditional empirical risk minimization principle employed by ANNs. This research presents a SVM-based CCP recognition model for the on-line real-time recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time. Empirical comparisons indicate that the proposed SVM-based model achieves better performance in both recognition accuracy and recognition speed than the model based on a learning vector quantization network. Furthermore, the proposed model is more robust toward background noise in the process data than the model based on a back propagation network. These results show the great potential of SVM methods for on-line CCP recognition.