An unsupervised learning neural algorithm for identifying process behavior on control charts and a comparison with supervised learning approaches

The applications of supervised pattern recognition techniques on control charts have shown a substantial improvement in the ability to utilize the information of the chart more effectively than conventional run rules. One major assumption underlying this methodology is that the user has a set of well-defined patterns to detect and a sufficient number of training examples. In practice, however, sufficient training examples may not be readily available, owing either to the inability to simulate these patterns or to the lack of real process data.